CCE -PROFICIENCE INDIAN INSTITUTE OF SCIENCE …

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INFORMATION HANDBOOK

August -December 2021

CCE -PROFICIENCE

INDIAN INSTITUTE OF SCIENCE

BENGALURU-560012

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Particulars Page

No.

Introduction 3

Instructions 5

Course Schedule 7

Fee Structure 9

Performa for Certificate 36

REGULAR COURSES

Sl.

No. Name of the Course Credit

1 Innovation and Design (Mon) (6 pm to 8 pm) 2:0

2 Quantum Theory (Mon & Wed) (6 pm to 7.30 pm) 3:0

3 Soil Dynamics (Mon & Wed) (6 pm to 7.30 pm) 3:0

4 Applied Linear Algebra for Engineers and Researchers (Sun) (10 am to 12 pm) 2:0

5 Service Design Thinking (Tue) (6 pm – 8 pm) 2:0

6 Vaccine and Immunity (Tue & Thu) (6 pm to 7.30 pm) 3:0

7 Vibration and Noise: Theory and Practice (Wed) (6 pm – 8 pm) 2:0

8 Probability Foundations for Machine Learning (Fri) (6 pm – 8 pm) 2:0

9 Analysis and Design of Composite Structures (Fri) (6 pm – 8 pm) 2:0

10 One and Two-Dimensional NMR Spectroscopy for Chemists (Sat) (10 am to 1 pm) 3:0

11 Artificial Intelligence (Sat) (10 am to 1 pm) 3:0

12 Data Structures and Graph Analytics (Sat) (10 am – 12 pm) 2:0

13 Project Management (Sat) (10 am to 12 pm) 2:0

14 Introduction to Computing for AI & Machine Learning (Sat) (10 am to 1 pm) 3:0

15. Deep Learning (Sat) (10 am to 1 pm) 3:0

16. Start-up Tools (SuT) (Sat) (10 am to 1 pm) 3:0

17. Structural Analysis and Design Optimization: Theory and Practice (Sat) (12 pm – 2pm) 2:0

18. Foundations of Data Science and Machine Learning (Sat) (1 pm to 4 pm) 3:0

19. Business Analytics with Management Science Models and Methods (Sat) (10 am – 1

pm) 3:0

20. Reinforcement and Deep Reinforcement Learning (Sat) (1 pm – 4 pm) 3:0

21. Mathematics for Machine Learning (Sat) (2 pm – 5 pm) 3:0

22. Introduction to AI & Machine Learning (Sat) (1 pm – 4 pm) 3:0

23. Fundamentals of Machine Learning (Sat) (10 am – 1 pm) 3:0

24. Embedded Systems and IoT Sensors Applications (Sat) (2 pm to 4 pm) 2:0

25. Foundations of Intellectual Property Rights (IPRs) – Legal and policy perspective (Sat)

(2 pm – 4 pm) 2:0

26. Machine Learning (Sun) (10 am to 1 pm) 3:0

CONTENTS

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INTRODUCTION

Indian Institute of Science (IISc) established in 1909, is a Deemed University and Centrally Funder

Technical Institution under the Department of Higher Education, Ministry of Human Resources

Development, Government of India. Rapid strides in science and technology make it imperative

that the education of professionals be continued over their entire career rather than be confined to a

single stretch. What is needed is a complete integration of education with work during their

productive life span, which will be adequate to help them cope with new demands. Continuing

Education embraces all the processes of education that one undergoes throughout a working life

and which have a relevance to the practical problems likely to be encountered in one’s career. It

may be realized through formal and informal modes of teaching, or through mass media. In recent

years, there has been a growing awareness on the part of Universities that imparting knowledge to

people beyond their boundaries is an equally important part of their service to the community. With

this broad perspective of their function in society, Universities have begun to seek ways of reaching

out to professionals. The IISc has evolved several mechanisms to make the expertise and facilities

available to qualified technical people in industries, Universities and research establishments. The

need for forging links between academic institutions and industries and R&D organizations has

been a goal set for the IISc by its illustrious founder, J.N. Tata. CCE-PROFICIENCE was

established with the objective of providing a sustained and rigorous continuing education program

offering courses on subjects of topical interest to scientists and engineers in and around Bangalore.

This program, believed to be the first of its kind in the country, is a joint venture between IISc and

several Professional Institutions/Societies in Bangalore. The program name signifies the coming

together of Professional Institutions and the Indian Institute of Science. It was started on an

experimental basis in 1980 and has proved to be extremely popular and has attracted wide attention

in academic and professional circles. The demand for some courses, especially on computers,

microprocessors and management is so overwhelming that it has not been possible to admit all the

Eligible applicants. Every year, there has been a steady increase in the number of students as well

as the types of courses offered indicative of the growing popularity of this Program. IISc is the

custodian of the academic standards of all CCE-PROFICIENCE courses. It has the responsibility

of evolving appropriate teaching norms, providing the venue and facilities for conducting courses,

organizing the tests and examinations and issuing certificates to the successful participants. These

tasks are coordinated by the Centre for Continuing Education (CCE).

COURSES

Continuing education program organized under CCE-PROFICIENCE offers semester long courses

in areas of topical interest. The courses are organized during evening hours so that working

professionals can participate without getting their normal work affected. All courses are normally

at the postgraduate level and many of these are in fact offered to the IISc students regularly.

Participants in certain selected courses are provided practical training in computer and other

laboratories, as appropriate. The course contents are regularly upgraded on the basis of feedback

from the faculty and the participants. Courses are offered during the period AUG-DEC and JAN-

MAY and around 15-20 courses are scheduled during each semester. Each course has lectures at

the rate of two or three hours per week depending upon the number of course credits. Tests and

examinations are conducted according to the IISc norms. A series of courses leading to different

specializations are offered in a sequential manner, especially in the area of Computer Science and

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Engineering. This would enable the participants who start with the entry level courses progress

towards more advanced ones and specialize in one of the streams.

EVALUATION

The total marks for assessment will be equally distributed between the seasonal work and end

semester examination. The seasonal work consists of class tests, mid semester examination, and

homework assignments etc. as determined by the instructor. The participants who maintain a

minimum of 75% attendance both in the theory and computer/laboratory classes will be evaluated

based on the combined performance in the end semester examination and seasonal work and

assigned a letter grade.

NO RE-EXAMINATION SHALL BE CONDUCTED UNDER ANY CIR

CUMSTANCES.

The letter grades carry a 10 point grading assessment as indicated below

Grade: A+ A B+ B C D F (Fail)

Grade Points: 10 9 8 7 6 5 0

CERTIFICATES

Certificates will be issued only to those who get at least a ‘D’ grade. Attendance certificates shall

not be issued to anyone. This being a continuing education program meant especially for self-

improvement, the credits accumulated cannot be equated with the credits earned through formal

education. There shall be no claims for CCE-PROFICIENCE credits being counted towards partial

fulfillment of credit requirements towards any degree/diploma or other formal recognitions offered

by IISc.

Formal Course completion certificates will not be issued under any circumstances to any candidate.

FACULTY

The instructors for the courses are mostly Institute Faculty. However, competent professionals

from other R&D organizations and industries are also involved in teaching some of the courses.

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INSTRUCTIONS

HOW TO APPLY:

Details of the courses are available online at cce.iisc.ac.in and also directly at CCE application portal

https://iisc.online/admissions/home.html . Essential Qualification for any course is a degree in

Engineering or a postgraduate degree in Science/Humanities as applicable with pre-requisites. Each

participant will be admitted for a Maximum of Two Courses. Applying to courses is strictly through

online portal of CCE. Please read all the instructions provided at our portal before applying.

Payment of course fee is through payment gateway provided at our online portal and no other means

of payment is accepted. The course fee is Rs. 5000/- per credit and registration fee is Rs. 300/- per

course. Any other gateway charges must be borne by participant during online payment. For each

application, participants must upload (BE, B.Tech / Post Graduation) Convocation/Degree

Certificate without fail. (Class conducted: Week days 6 pm. to 8 pm) & (Saturday’s 10 am to 1 pm

& 2 pm to 4 pm)

FEES

The course fee is Rs. 5000/- per credit. Some of the courses include a limited exposure to computer

operation and programming / Lab Fee (C). The additional fees of this are Rs. 5,000/- The course fee

and laboratory fee should be paid in full at the time of joining the course.

REFUND OF COURSE FEE

Refund of course fee will not be made, unless the course is withdrawn officially, in which case, the

course fee paid will be refunded in full. Application registration fee once paid will NOT BE

REFUNDED under any circumstance. Refund of fees in case of dropped courses will take minimum

3-4 weeks.

CLASSES

Classes will be held online via Microsoft Teams.

LABORATORY CLASSES

The timings and days for laboratory classes will be fixed in the second week of the respective

months (August & January) after the complete registration is known. This will be done, keeping in

view the convenience of the faculty and all the students of the courses with laboratory component.

RESULTS

Results of the courses will be announced normally around 1st week of January for August-

December term and 1st week of May for January-May term. Certificates will be issued on or after

the date of announcement of results and against surrendering the Identity Card.

NO REQUEST FOR CHANGE OF EITHER THE STIPULATED DATES, MODE OF

PAYMENT, CHANGE OF COURSE OR SUBMISSION/VERIFICATION OF

ENCLOSURE TO APPLICATION ETC., WILL BE ENTERTAINED UNDER ANY

CIRCUMSTANCE

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Schedule of Online Courses for August – December 2021 Sl.

No. Name of the Course Credit Faculty Department

1. Innovation and Design (Mon) (6 pm to 8 pm) 2:0

Prof. P Achutha Rao

(Retd.) and Dr. J E

Diwakar (Retd.)

NID, R&D and

CPDM, IISc

2. Quantum Theory (Mon & Wed) (6 pm to 7.30

pm) 3:0 Prof. Tanmoy Das

Department of

Physics, IISc

3. Soil Dynamics (Mon & Wed) (6 pm to 7.30

pm) 3:0 Prof. Jyant Kumar

Dept. of Civil

Engg., IISc

4. Applied Linear Algebra for Engineers and

Researchers (Sun) (10 am to 12 pm) 2:0

Mr. M Krishna

Kumar (Retd.),

Dr. Arulalan

Rajan and Dr.

Ashok Rao

(Retd.)

Dept. of ESE

(CEDT), NITK

Surathkal &

DESE

5. Service Design Thinking (Tue) (6 pm – 8 pm) 2:0

Prof. P. Achutha

Rao (Retd.), Prof.

T V P Chowdry &

Dr. J E Diwakar

(Retd.)

CPDM, NID

R&D Campus &

CST

6. Vaccine and Immunity (Tue & Thu) (6 pm to

7.30 pm) 3:0

Prof. Dipshikha

Chakravortty

Dept. of MCB

IISc

7. Vibration and Noise: Theory and Practice

(Wed) (6 pm – 8 pm) 2:0 Dr. S B Kandagal AE, IISc

8. Probability Foundations for Machine Learning

(Fri) (6 pm – 8 pm) 2:0

Mr. M Krishna

Kumar, (Retd). Dr.

Ashok Rao, & Dr.

Arulalan Rajan

DESE, IISc &

NIT, Surathkal

9. Analysis and Design of Composite Structures

(Fri) (6 pm – 8 pm) 2:0 Dr. G Narayana

Naik AE, IISc

10. One and Two-Dimensional NMR Spectroscopy

for Chemists (Sat) (10 am to 1 pm) 3:0

Prof. N.

Suryaprakash

NMR Research

Centre, IISc

11. Artificial Intelligence (Sat) (10 am to 1 pm) 3:0

Prof. M Narasimha

Murthy (Honorary

Professor)

CSA, IISc

12. Data Structures and Graph Analytics (Sat) (10

am – 12 pm) 2:0 Prof. Y N Srikant CSA, IISc

13. Project Management (Sat) (10 am to 12 pm) 2:0

Prof. T V P

Chowdry and Dr.

J. E. Diwakar

CST and CPDM,

IISc

14. Introduction to Computing for AI & Machine

Learning (Sat) (10 am to 1 pm) 3:0

Prof. Sashikumaar

Ganesan

Dept. of CDS,

IISc

15. Deep Learning (Sat) (10 am to 1 pm) 3:0 Dr. V. Susheela Devi Dept of CSA,

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IISc

16. Start-up Tools (SuT) (Sat) (10 am to 1 pm) 3:0

Dr. R.N. Narahari

and Prof SA

Shivashankar

CeNSE,IISc

17. Structural Analysis and Design Optimization:

Theory and Practice (Sat) (12 pm – 2pm) 2:0 Dr. S B Kandagal AE, IISc

18. Foundations of Data Science and Machine

Learning (Sat) (1 pm to 4 pm) 3:0

Dr. Deepak N

Subramani

Dept. of CDS,

IISc

19. Business Analytics with Management Science

Models and Methods (Sun) (10 am – 1 pm) 3:0 Dr. M Mathirajan

Dept. of MS,

IISc

20. Reinforcement and Deep Reinforcement

Learning (Sat) (1 pm – 4 pm) 3:0

Prof. Shalabh

Bhatnagar CSA, IISc

21. Mathematics for Machine Learning (Sat) (2 pm

– 5 pm) 3:0

Prof. Kunal

Narayan

Chaudhury

EE, IISc

22. Introduction to AI & Machine Learning (Sat)

(1 pm – 4 pm) 3:0

Dr. Pradipta

Biswas CPDM, IISc

23. Fundamentals of Machine Learning (Sat) (10

am – 1 pm) 3:0

Dr. Gopal Krishna

Sharma, Dr.

Badarinath Ambati

& Prof. M Sekhar

Fiserv India

Ltd., Altair

Engineering, &

Civil Engg.

24. Embedded Systems and IoT Sensors

Applications (Sat) (2 pm to 4 pm) 2:0

Mr. M Krishna

Kumar (Retd.) &

Mr. S M

Narasimhan

DESE, IISc

25.

Foundations of Intellectual Property Rights

(IPRs) – Legal and policy perspective (Sat) (2

pm – 4 pm)

2:0

Prof. Anjula

Gurtoo & Dr

Akriti Jain

Dept. of MS

26. Machine Learning (Sun) (10 am to 1 pm) 3:0

Prof. M Narasimha

Murthy (Honorary

Professor)

CSA, IISc

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FEE STRUCTURE AT A GLANCE

Regular Courses

Per Credit: Rs.5, 000/-

Computer Lab Fee: Rs.5, 000/-

1. Course with 2 credits# Rs. 10,000/-

2. Course with 2+C credits # Rs. 15,000/-

3. Course with 3+0 credits # Rs. 15,000/-

4. L Stands with 2+L Credits # Rs. 15,000/-

# Credits = Lecture Hours per week

$C Stands for Computer Laboratory

$L Stands for Online Course

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01. Online Course on Innovation and Design (2:0)

Objectives:

Innovation is imperative. Innovation is a main driver of economic progress and social well-being. Innovation

typically suggests the commercialization of new ideas in ways that create social and economic values.

Innovation is also synonymous with creativity, design, artistic imagination, and cultural change. Most

management practices seek predictability, innovation is dealing with possibilities. Transforming from

'Predictability- centric' to 'POSSIBILITIES- centric' culture is a struggle. There is an urgent need to discard the

past proven methods, and to create a new culture of innovation across the organization. There can be design

without innovation, but there is no innovation without Design. To succeed, organization has to integrate

Design. This Course, through theory classes, aims to look at these issues and create an awareness of innovation

by design and various design methods in the manufacturing context.

Syllabus:

Design, Design Thinking, Innovation; Creativity Thinking, Skills and methods; New Product Development -

Quality Function Deployment, Value Engineering, Design to Cost, Design for Assembly, Design for Service,

Failure Modes and Effects Analysis; TRIZ (Systematic Innovation)- Overview; Concept Generation Methods,

Concept Selection Methods.

Target Group:

Engineers working in manufacturing industries, Practicing Engineers, Managers involved in Innovation and

New Product development, Design and Development in Industries, R & D Organizations etc., Academic

Personnel in teaching/practicing Product design, Product engineering, Design and Development and fresh

engineers interested in Design and Innovation.

Faculty:

Dr. J E Diwakar

(Retd.)

CPDM

lISc., Bengaluru.

Email: [email protected],

Faculty:

Prof. P Achutha Rao Retired from NID R & D

Campus, Bengaluru. E-Mail:

[email protected]

Reference Books:

1. Thomas Lockwood, Edgar Papke; Innovation by

Design; New Page Books,2017

2. Larry Kelly, Helen Walters; Ten types of Innovation,

Wiley, 2013

3. Vijay Kumar,101 Design Methods; Wiley,2012

4. Top Kelly, The Art of Innovation; Profile, 2016

Who can apply?

Graduation in Engineering, Design (B.

Des) & Post-Graduation in Management and

Design after engineering

Course Fee: Rs. 10,000/- + 18% GST

Online Seats are Limited to 100

Online Classes using Microsoft Teams

Schedule: Monday’s - 6.00 pm. to 8.00 pm

mailto:Email:

mailto:[email protected]


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02. Online Course on Quantum Theory (3:0)

Objectives:

This course is aimed at post BE and post MSc academicians as well as for those advanced students and young

faculties and R&D personals who are interested to learn, revise, and/or obtain a different perspective on

the beautiful paradigm of quantum mechanics which shook the world in the early 20th century. For the

young faculties who are in teaching profession, the course might help them getting a different and

alternative perspective to teach, think, and learn quantum theory. I will also extend the quantum theory to

current research topics of non- conserving, non-Hermitian systems

Syllabus:

Historical foundations and experimental facts. Converting Classical mechanics to Quantum mechanics:

Classical Hamiltonian becoming quantum Hamiltonian, Noether Symmetries giving uncertainly principles,

Poisson bracket becoming Commutator, Liouville's theorem becoming Schrodinger equation. Converting

Statistical Mechanics to quantum mechanics: Probabilistic description of dynamics, Building ensemble

density with wave function, Probability current. Postulates of quantum mechanics. Wave packets/Coherent

States. One-dimensional problems: step, barrier and delta-function potentials. Tunnelling, scattering and

bound states. Mathematical preliminaries: Vector space, Hilbert space, Operator, Hamiltonian, Eigenvalue.

Harmonic oscillator, operator approach. Matrix formulation of quantum mechanics. Hermitian and unitary

operators. Orthonormal basis. Non-Hermitian Hamiltonian, Exceptional points, Bi orthogonal basis

Heisenberg representation. Ehrenfest's theorem. Three-dimensional problems. Rotations, angular

momentum operators, commutation relations. Spherical harmonics. Hydrogen atom, its spectrum and wave

functions. Symmetries and degeneracies. Spin angular momentum. Spin-1/2 and two-level systems.

Addition of angular momentum. Spin-orbit and hyperfine interactions. Time independent perturbation

theory. Stark and Zeeman effects. Variational methods, ground state of helium atom.

Target Group:

Not directly relevant to Industry

Faculty:

Dr. Tanmoy Das Department of Physics

Indian Institute of Science,

Bangalore 560012

[email protected], [email protected]

Reference Books:

1. Quantum mechanics, Albert Messiah, Dover

publishers, 1999

2. Quantum mechanics, Eugen Merzbacher,

Willey,1970

3. Principles of Quantum

Mechanics, Ramamurti Shankar, Plenum Press,1980

Who can apply? BE/M.Sc

Pre- Requisites: Classical Mechanics




Schedule: Monday and Wednesday

(6.00PM to 7.30PM)


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03. Online Course on Soil Dynamics (3:0)

Objectives:

To train a Civil Engineering Professional in the field of Vibration, Wave Propagation, Determination of Dynamic

Properties of Soils, Earthquake Engineering, Machine Foundation Design, Determination of Liquefaction Resistance,

Vibration Isolation, On-destructive testing by using different Geophysical Methods. Seismic Bearing Capacity of

Foundations.

Syllabus:

Fundamental of vibrations; analysis of free and forced vibrations using spring dashpot model; equations' formulation and

solution; block vibration test for determining stiffness and damping coefficient of soil mass; formulation of the problem

for the multi-degree freedom system. Wave propagation in bar and elastic media; different types of waves; dynamic tests

for determination of elastic and shear modulus. Theories for foundations on elastic half space; effect of different pressure

distribution; comparison with spring-dashpot model. Geophysical survey using reflection, refraction, steady state

vibration, spectral analysis of surface waves (SASW) & multichannel analysis of surface (MASW) tests, and cross hole

shear tests. Liquefaction analysis; cyclic shear test; assessment of zone of liquefaction. Seismic bearing capacity of

foundations and seismic earth pressures. Vibration isolation.

Target Group:

All teachers from different engineering Colleges including NIT's and liT’s, Central Road Research Institute, New Delhi,

Central Building Research Institute, Roorkee, CSRMS, New Delhi, Civil/Geotechnical engineers from different public

undertaking and pvt.companies {BHEL, Coal Indian Limited, NBCC, NHPC, NTPC, Engineers, Engineers India Limited

, Stup Consultant, AFCON,Indian Geotechnical Society.)

Faculty:

Prof. Jyant Kumar

Dept. of Civil Engineering

Indian Institute of Science

Bengaluru (lndia)-560012

[email protected]

Reference Books:

• Richart, F.E., Woods, R.D.and Hall, J.R. Vibrations of

soils and foundations. Prentice-Hall,1970.

• Major, A Vibration Analysis and Design of

Foundations for Machines and Turbines.Collets,1962.

• Kramer, Steven L. Geotechnical Earthquake

Engineering. Prentice Hall,

653 pp.

Who can apply?

B.Tech-Civil Engineering

Pre- Requisites: B.Tech-Civil Engineering




Schedule Monday and Wednesday

(6.00PM to 7.30PM)

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04. Online Course on Applied Linear Algebra for Engineers and Researchers

(2:0)

Objectives:

This course aims at providing an in depth understanding of Linear Algebra and applications in various domains like image

processing, data fitting, data classification and machine learning. The focus of the course is primarily on geometric approach

to linear algebra and applications that involve concepts of linear algebra.

Syllabus:

System of Linear Equations, Vector spaces, Linear independence, basis, rank, linear transformations, Inner products,

Orthogonality, Orthonormal basis, Orthogonal projections and Orthogonal complements, Matrix decompositions -

eigenvalues and eigenvectors, QR, SVD, Vector calculus and gradients of matrices. Applications: Discrete Fourier

Transform, Discrete Cosine Transform and applications in image processing, least squares curve fitting, least squares

classification, Subspace’s techniques and PCA.

Target Group:

Engineers and researchers in working in the domains of Computer Vision, Machine Learning, Communication Engg,Signal

Processing, Data Science etc.

Faculty: Mr. M Krishna Kumar

(Retd.), PRS., Dept. of

ESE (CEDT), IISc,

Bengaluru

Email.

mkkumarcedt@gmai

l.com

Faculty:

Dr. Arulalan Rajan, Formerly, Assistant

Prof., Dept. of E& C

Engg., NITK.,

Surathkal

Email:

[email protected]

Faculty: Dr. Ashok Rao,

Formerly Head, Networking Project,

Dept. of ESE (CEDT),

IISc., Bengaluru

Email: ashokrao.mys@gmail.

com

Reference Books:

1. Gilbert Strang, Introduction to Linear Algebra,

Wellesley Cambridge Press,5th Ed., 2015.

2. Stephen Boyd, Introduction to Applied Linear

algebra, Cambridge University, 2018

3. Deise north, Mathematics for Machine

Learning, Cambridge University Press, 2020.

4. NPTEL lectures of Prof. Vittal Rao on

Advanced Matrix Theory and MIT Open

Courseware lectures of Prof. Gilbert Strang on

Linear algebra.

Who can apply?

BE/B.Tech

Pre- Requisites:

Mathematics at undergraduate level




Schedule: Sunday’s – 10.00am to 12.00pm






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05. Online Course on Service Design Thinking (2:0)

Objective

The globalization and digital connectivity have forced many organizations to look at the way new products/services are to

be developed for customer acceptance in the changed competitive “global digital world’. The economy is shifting from

manufacturing economy to service and knowledge economy. Service economy is an economy based on providing services

rather than manufacturing or producing goods. There is increased importance of the service sector in industrialized

economies. The current list of fortune 500 companies contains more service companies and fewer manufactures than in

previous decades. Many products are being transformed into services. Design of services is gaining more prominence and

becoming a specialized field of expertise. This course will look at various aspects of service design thinking. The participants

will become aware of all aspects of service design.

Syllabus:

• Innovation, Creativity, Embedded Mindset to Creative Thinking, Barriers to Creativity.

• Product Economy to Service Economy

• Service Design Thinking

• Fields of Service Design

• Principles of Service Design

• Marketing Connecting with people, Creating Customer Value

• Product Design: Developing Products with Service Applications

• Social Design: Delivering Positive Social Impact

• Strategic Management

• Operations Management

• Tools of Service Design Thinking

Target Group:

Practicing Engineers, Managers Responsible for developing engineering services, Professional in Design and Development

in Industries, R & D Organizations etc., Academic Personal in teaching/practicing Product design/Service design, Product

engineering, Design and Development and fresh engineers interested in Design and Innovation; Start up entrepreneurs.

Faculty:

Dr. J. E. Diwakar (Retd.)

Dept. of CPDM.,

IISc., Bengaluru.

Email: [email protected]

Faculty:

Prof. P. Achutha

Rao (Retd.) NID R & D Campus.

E Mail: [email protected]

Faculty:

Prof. TVP

Chowdry Project Scientist CST

E mail: [email protected]

1.

Reference Books:

1. Marc Stickdorn,

This is Service Design Thinking: Basics-Tools-Cases.

Wiley.

2. Andy Polaine, Rosenfeld Media,

Service Design from Insight to Implementation.

3. Tim Brown,

Change by Design: How Design Transforms Organizations

and Inspires Innovation.

4. Satu Miettinen & Mikko Koivisto,Designing Services

with Innovative Methods: University of Art and Design

Helsinki

Who Can apply?

Graduation in Engineering, Any Master’s

Degree after engineering.




Schedule: Tuesday’s: 6.00 pm. to 8.00 pm



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06. Online Course on Vaccine and Immunity (3:0)

Objectives:

This course will deal with in depth understanding of vaccine, vaccine development, vaccine

engineering and finally about the immunity by vaccines

Syllabus:

History of Vaccine development, Types of vaccines, bacterial and viral vaccine, Genetic engineering techniques to develop vaccine, how vaccine works.

Target Group:

Everybody in Biotech industry with vaccine manufacturing, will benefit,

Faculty:

Prof. Dipshikha Chakravortty

Professor

Department of Microbiology and Cell Biology


Bangalore 560 012 [email protected]

Reference Books:

1. Kuby's Immunology, Janis Kuby, New York WH

Freeman publications Principles of Vaccination,

CDC, Updated on 2020

Who can apply?

M.Sc in any branch of Science




Schedule Tuesday and Thursday (6.00PM to

7.30PM)

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07. Online Course on Vibration and Noise Control: Theory & Practice (2:0)

Objectives: Growing awareness of vibration, noise and harshness feeling has necessitated the valid design criterion in the design of

machines, automobiles, buildings, industrial facilities, etc., and the increasing number of standard regulations and human

comfort associated with noise, harshness and vibration makes it mandatory to control vibration and noise leading to quieter

technology in pumps, engines, compressors, chillers and other consumer products. There is a great demand to enhance ride

comfort of bikes, cars, aircrafts and other automobiles. Vehicle Dynamics basics and rowing awareness about noise pollution

among the consumer necessitates the OEM companies to stress upon the products without NVH problems. Analytical,

MATLAB and FEM based tools such as ANSYS, NASTRON, ABACUS and SYSNOISE helps to achieve the goals of NVH

study. This course is for engineers/scientists/entrepreneurs/instructors in the industries/institutes to learn the analytical and

experimental skills to tackle the problems related noise, vibration and harshness (NVH) during design and manufacturing

stage for technically superior and commercially viable product to achieve “EMPOWER INDIA WITH SKILL AND

Knowledge”

Syllabus:

Vibration of structural systems. SDOF, 2-DOF, MDOF and continuous systems. Eigen values and vector estimation methods. Free and

Forced vibration analysis. Torsional vibration and applications. Damping estimation methods

Structural Vibration control elements: isolation, damping, balancing, resonators, absorption, barriers and enclosures. Vibration and

noise standards. NVH measurement tools and techniques. Modal parameter (natural frequency, mode shape and damping) estimation

techniques. Signal and system analysis.

Demonstration of vibration and noise experiments – beam, plates, impulse excitation, electrodynamic shaker excitation, FFT analyzer,

stroboscope and mode shape animation, sound level meter, microphones. Vibration transfer function (VTF) and noise transfer function

(NTF)Noise and its effects on man. Acoustic and sound field. Enclosures, shields and barriers-design. Silencer and suppression systems.

Noise level interpolation and mapping. Harshness effects and measurements and solutions. NVH Parameters related to vehicle dynamics

Case studies discussion (vibration reduction in passenger car, tiller, tractors, steering column/wheel vibration diagnosis, Modal analysis

of Helicopter, Vibration diagnosis in diesel engine power plant, rotodynamic analysis of DWR and tracking antenna and engine and

compressor noise attenuation and vibration isolation, engine-compressor mount design, vibration diagnosis in power plants, gear shift

harshness, newspaper printing cylinder vibration diagnosis, engine filter bracket dynamic analysis, noise reduction for mixer grinders, field

audit of industrial chimney for wind induced vibration, stability studies of sports bike, aerodynamic stability derivatives of scaled model

of aerospace vehicles)

Target Group:

Mechanical, Civil, Aerospace, Automotive, Industrial Engineers, Construction Technologists, R & D Labs, New product Design and

Development Groups, Entrepreneurs and Engineering College Instructors. Professionals to pursue Postgraduate and Higher Studies

Faculty:

Dr. S B Kandagal Principal Research Scientist,

Dept. of AE.,

IISc., Bengaluru.


Reference Books:

1. Harris, C.W”,

Shock and Vibration Handbook”

McGraw Hill, New York, 2012.

2. Ewins, D.J.

” Modal analysis: Theory and Practice”,

Research Studies Press Ltd, England, 2014

3. Gillespie, T.D.,

“Fundamentals of Vehicle Dynamics”,

Society of Automotive Engineers. Inc, 2010.

4. Beranek, L.L,

” Noise and Vibration Control”, Wiley, 2008

Who Can apply?

B.E / ME / MSc / AMIE OR equivalent




Schedule: Wednesday’s - 6.00 pm. to 8.00 pm.


Page 16 of 36

08. Online Course on Probability Foundations for Machine Learning (2:0)

Objectives:

This course aims to provide an in Depth Understanding of Probability Concepts for Engineers and Professionals

Working in Various Domains Including Machines Learning, Data Science.

Syllabus:

Probability and Counting Conditional Probability Independence of events, Discrete and Continuous Random

Variables and their Distributions, Independence of Random Variables, Joint Distributions Joint Expectations

Covariance Moments, Conditional Expectations, Limit Theorems Random Vectors, Covariance Matrix, Sample

Geometry and Random Sampling, Multivariate Normal Distribution.Parameter Estimation Regression Models.

Random Processes, Stationarity, Markov Chains, Bernoulli and Poisson Processes, Some Applications.

Target Group:

Engineers and Researchers.

Faculty: Mr. M Krishna Kumar

(Retd.),

PRS., Dept. of ESE (CEDT),

IISc., Bengaluru

Email.

[email protected]

m

Faculty: Dr. Arulalan Rajan,

Formerly, Assistant

Prof., Dept. of E& C Engg.,

NITK.., Surathkal..


Faculty: Dr. Ashok Rao,

Formerly Head, Networking Project,

Dept. of ESE (CEDT),

IISc., Bengaluru

Email: ashokrao.mys@gmail.

com

Reference Books:

1. Bertsekas, Tsitsiklis

Introduction to Probability, 2nd Ed. Athena Scientific.

2. Roy D Yates, D J Goodman

Probability and Stochastic Processes, John

Wiley. 2014.

3. Steven M Kay

Intuitive Provability and Random Processes, Springer.

4. R. Johnson, D.A Wichern,

“Applied Multivariate Statistical Analysis”, Pearson,

2015.

Who can apply?

BE/ MSc.

Pre-requisites: Mathematics at Graduate Level




Schedule: Friday’s -6.00 pm - 8.00 pm.






Page 17 of 36

09. Online Course on Analysis and Design of Composite Structures (2:0)

Objectives:

Composites are future materials and have been finding applications in all fields of Engineering (Aero, Civil, Mechanical,

Automobile, Marine, Chemical, Electrical, Electronics etc). Many FEM software packages like ANSYS, MSC-NASTRON,

PATRAN, ABACUS, LS-DYNA, etc are available for Analysis & Design Optimization. One should first understand the

Mechanical behavior of the Composite Structures before using FEM packages. After the completion of this course one can

use the FEM software packages for better quality of professional work and optimum usage of time, computing and human

resources. Syllabus:

Introduction: Basic Concepts and Terminology, different types of fibers and matrices, their properties and applications.

Micromechanics of Composites: Prediction of Elastic and strength properties, Micromechanical failure etc.

Macromechanics of Lamina: The theory of elasticity, Constitutive equations of a lamina, transformations, numerical

examples.

Failure theories for composite lamina, numerical examples.

Mechanics of Laminated Composites: ABD matrices, etc. Hygrothermal Analysis

Analysis of Composite Beams: theory and numerical examples.

Bending Analysis of Beams,

Analysis of Laminated composite plates: Classical and first order theories, Energy Method, numerical examples.

Buckling analysis of plates: theory and numerical examples.

Design of laminates using Carpet plots, AML plots, laminate design using numerical examples.

Target Group:

1. Technologists/ Engineers/ Scientists/ Trainees/ Project Staff/ etc. from Industries, R & D Organizations, Institutions,

Colleges etc.

2. Faculty of Engineering// Diploma Institutions etc.

3. Fresh Graduates, Post Graduates, Ph.D. Students, Research Fellows, SRFs, JRFs, etc.

Faculty:

Dr. G. Narayana Naik Principal Research Scientist,

Dept. of AE.,

IISc., Bengaluru.


Reference Books:

1. Madhujit Mukhopadhyay, Mechanics of Composite

Materials and Structures- Universities Press- Engg.

2004.

2. Zafer Gurdal, Raphael T Haftka, Design and

Optimization of Laminated Composite Materials,

John Wiley & Sons, INC – 1999.

3. J.N.Reddy, Mechanics of Laminated Composite

Plates and Shells Theory and Analysis – CRC Press

– 2004.

Who Can apply?

B.E / B.Tech. / AMIE / M.Sc.(Engg.)/ AMAeSI

(Engg.) (Mechanical, Aero, Civil, Automobile,

Marine, Ocean) OR equivalent.




Schedule: Friday’s - 6.00 pm. to 8.00 pm.


Page 18 of 36

10. Online Course on One- and Two-Dimensional NMR Spectroscopy for

Chemists (3:0)

Objectives:

NMR spectroscopy is a ubiquitous technique employed in various branches of science and engineering. Today

it is hard to do experimental chemistry and biology without the utility of NMR spectroscopy. The real strength

of NMR lies in probing the very weak interactions surrounding the resonating spins. It provides information on

the molecular structure, conformation and dynamics. It is essential for all the practicing chemists and research

scholars to clearly understand the concepts and interpretation of NMR spectra. At present the deeper

understanding of NMR spectroscopy is residing with only few active NMR groups of the country. This course

will provide exhaustive knowledge to the participants about the latest state of art of the NMR spectroscopy.

Syllabus:

In this course, I will discuss fundamental concepts of NMR spectroscopy, experimental determination of NMR

spectral parameters, their interpretation, selective and broadband homo and Heteronuclear spin decoupling,

numerous examples of the analysis of NMR spectra of 1H, 13C and other heteronuclear will be given. The

relaxation processes, their measurement and utility in understanding molecular dynamics, the polarization

transfer mechanism, the spectral editing techniques, such as, APT, DEPT, INEPT will also be discussed. Two-

dimensional NMR and the commonly employed experiments, viz., COSY, TOCSY, HSQC, HMQC, HMBC,

NOESY, etc will be discussed with number of examples. The practical aspects of one- and two-dimensional

NMR data acquisition and processing will also be highlighted. The solid-state NMR, magic angle spinning and

cross polarization will also be discussed.

Target Group:

All R and D scientists of Pharma industries, MSc students, PhD students and also practising young researchers

Faculty:

Prof. N. Suryaprakash

NMR Research Centre,

Indian Institute of Science,

Bangalore 560012

[email protected]

Reference Books:

1. A Complete Introduction to Modern NMR

Spectroscopy: RobertS Macomber

2. Carbon-13 NMR Spectroscopy, Eberhard Breitmeier

and Wolfgang Voelter

3. High Resolution NMR, Theory and Chemical

Applications, Edwin D. Becker

4. High Resolution NMR Techniques in Organic

Chemistry, Timothy D Claridge

5. NMR Spectroscopy Explained, Neil E Jacobsen

Who can apply?

MSc in chemistry, M Pharm, MSc in

Physics




Schedule: Saturday’s - 10.00 am. to 1.00 pm


Page 19 of 36

Objectives:

To introduce both the symbolic and connectionist paradigms of Artificial Intelligence. The course will

be self-contained. The aim is to cater to the needs of both researchers and developers.

To introduce both the symbolic and connectionist paradigms of Artificial Intelligence. The course will

be self-contained. The idea is cater to the needs of both researchers and developers.

Syllabus:

Search for problem solving. Inference based on Logic and Probability; Theorem Proving and Prolog.

Data Structures and LISP. Machine Learning: Neighborhood based, and Linear Discriminants. Neural

Networks and Back Propagation. Deep Learning

Target Group:

Any academic or R&D organisation with potential interest in AI.

.

11. Online Course on Artificial Intelligence (3+0)

Faculty:

Prof. M Narasimha Murty

Honorary Professor,

Dept. of CSA.,

IISc, Bengaluru.


Reference Books:

1. Russell and Norvig, Artificial Intelligence: A

Modern Approach, Pearson,2015.

2. M N Murty and VS Devi, Introduction to

Pattern Recognition and Machine Learning,

lISe Press, World-Scientific,2015.

3. Mitesh Khapra,Deep Learning, NPTEL, lIT

Madras, 2018.

Who can apply?

BE/B.Tech or its equivalent in any branch;

Master of Science or equivalent in Maths, Physics,

Computer Science, Statistics; or MCA.;

Pre-requisites:

A good background of college-level mathematics and

programming.




Schedule: Saturday’s – 10.00 am. to 1.00 pm


Page 20 of 36

12. Online Course on Data Structures and Graph Analytics (2:0)

Objectives:

Graph Analytics is important in different domains: Social Networks, Computer Networks, and Computational

Biology to name a few. This course will deal with the data structures and algorithms underlying Graph Analytics.

Important applications will also be considered.

Syllabus:

Overview of data structures such as arrays, linked lists, and trees. Important data structures such as binary search

trees, balanced trees, MFSET, heap, hash tables, game trees, etc. Algorithm design paradigms: back-tracking, divide

and conquer, dynamic programming, and greedy. Graph analytics: shortest path computation, minimum spanning

trees, graph matching, network flows, centrality computations, community detection, connection analysis, etc.

Target Group:

Persons in R&D and final year M.Tech. Students.

Faculty:

Prof. Y.N. Srikant,

Dept. of CSA

IISc., Bengaluru.

Email: [email protected], [email protected]

Reference Books:

1. T H Cormen, C E Leiserson, and R L Rivest,

Introduction to Algorithms, The MIT Press,

Cambridge, Massachusetts, USA, 1990.

2. Unnikrishnan, Cheramangalath, Rupesh Nasre, and Y

N Srikant, Distributed Graph Analytics:

Programming, Languages, and their Compilation,

Springer, 2020.

3. Amy E, Hodler, and Mark Needham, Graph

Algorithms: Practical Examples in Apache Spark and

Neo4j. O’Reilly, 2019 (free book, downloadable).

Who Can apply?

MCA/MSc/BTech/MTech (any discipline) with

good knowledge of programming in

C/C++/Java/Python (any one)

Pre-requisites:

Good knowledge of programming in

C/C++/Java/Python (any one)


Seats are Limited to 100


Schedule: Saturday’s - 10.00 am. to 12.00 pm


Page 21 of 36

13. Online Course on Project Management (2:0)

Objectives:

This course is envisaged to develop the understanding and skills for planning, scheduling and controlling

projects. This course provides a systematic and thorough introduction to all aspects of project management.

Students explore project management with a practical, hands-on approach through case studies and class

exercises. The understanding and skills gained in this course will help in Project Management Institute’s (PMI)

Project Management Professional (PMP) Program.

Syllabus:

• What is Project Management?

• Organizing Project Management Office & Team

• Project Planning, Work Break Down Structure

• Project Budgeting- Cost Estimation

• Project Scheduling- Gantt Chart PERT/CPM

• Project Resource Allocation- Fast Tracking- Crashing o Resource Loading & Levelling

• Project Management & Controlling & Earned Value Scope Creep and Change Control

• Project Evaluation & Termination

Target Group:

Practicing Engineers, Scientists, R&D Managers, Construction Managers, Architects, Designers, Professionals

from Knowledge & IT Industries, Entrepreneurs

Faculty:

Dr.J. E. Diwakar Dept. of CPDM., IISc.,

Bengaluru


Faculty: Prof TVP Chowdry. Project Scientist

CST, IISc

Email:

[email protected]

Reference Books:

1. Meredith R Jack, & Mantel J Samuel, Project

Management: International Student Version, Eighth

Edition, Wiley,2006, ISBN 978-81-265-3708-2

2. Project Management Institute, A Guide to the project

Management Body of Knowledge: {PMBOK® Guide)

Sixth edition, PMI,2018, ISBN 9781628253825

3. Charted Management Institute, Successful Project

Management, 2nd edition Elsevier, 2004, ISBN 0-7506-

64197

Who can apply?

Graduation in

Engineering/Architecture/Design or Post

Graduation in Science/Management




Schedule: Saturday’s 10.00 pm. to 12.00pm



Page 22 of 36

14. Online Introduction to Computing for AI & Machine Learning (3:0)

Objectives:

This course is aimed at building the foundation of computational thinking with applications to Artificial

Intelligence and Machine learning (AI & ML). Besides, how to build a neural network and how to train, evaluate

and optimize it with TensorFlow will also be covered in this course.

Syllabus:

Programming Foundation: Digital storage of data in computers, memory and data representation, Overflow and

Underflow, Round-off errors, the performance of a computer, Caches, Debugging and Profiling, Basic

optimization techniques for serial code.

Introduction to Python: Object and Data Structure Basics, Python Statements, Methods and Functions, Object-

oriented programming (OOP): Inheritance, Encapsulation, Abstraction, Polymorphism. OOP concepts in Python.

Python tools for Data Science: Pandas, NumPy, Matplotlib, Scikit-Learn, Just-in-Time (JIT) compilers, Numba

Computational Thinking: Arrays, Matrix-Vector, Matrix multiplication, Solving dense and sparse systems.

Deep Learning with TensorFlow: Tensors, Install TensorFlow, TensorFlow basics, Simple statistics and

plotting, Loading and exploring data, Learning with TensorFlow and Keras,Mini-project.

Target Group:

Aspiring ML/AI and Data scientists

Faculty:

Prof. Sashikumar Ganesan

Associate Professor & Chairman,

Dept. of Computational and Data Sciences,

IISc., Bengaluru.

E-mail: [email protected]

Reference Books:

1. John Hennessy David P Patterson.Computer

Architecture.AQuantitative Approach.6th edition,

Morgan Kauffman, 2017.

https://www.elsevier.com/books/computer-

architecture/hennessyI 978-0-12-811905-1

2. Shaw, Zed A. Learn python 3 the hard way. A very

simple introduction to the terrifyingly beautiful

world of computers and code. Addison-Wesley

Professional, 2017.

3. Aurelien Geron, Hands-On Machine Learning with

Scikit-Learn, Keras,and TensorFiow,2nd Edition,

O'Reilly Media, Inc.2019

Who can apply?

Any Engineering or Masters graduates

Pre-requisites:

Basic knowledge of programming




Schedule: Saturday’s (10.00 AM to 1.00

PM)

http://www.elsevier.com/books/computer-architecture/hennessyI



Page 23 of 36

15. Online Course Deep Learning (3:0)

Objectives:

To expose students to the concepts of deep learning. To educate them about the architectures used in

deep learning and their implementations. They will learn about architectures such as convolution

networks, recurrent neural networks, and generative models. Implementation for different architectures

and applications will be an important part of the course.

Syllabus:

Feedforward networks, convolution networks, recurrent neural networks, generative models, variational

and adversarial networks; practical issues in implementation for different applications such as text,

speech and images.

Target Group:

Industry, R&D organization, academic institutions

Faculty:

V. Susheela Devi Department of Computer Science and Automation,

lISe

Mob:9480436301

email: [email protected]

Reference Books:

• Goodfellow, Bengio and Courville, Deep Learning,

MIT Press, 2016

Who can apply?

B.Tech, M.Sc.(Computers), MCA

Pre-requisites:

Linear Algebra, Probability




Schedule: Saturday’s (10.00 am to 1.00

pm)


Page 24 of 36

16. Online Course on Start-up Tools (SuT) (3:0)

Objectives:

The aim of this Course is to introduce aspirants to the broad subject of Start-up initiatives. ‘Start-up’ is the

buzz word being sprouting now and has a great potential to grow into a big wood in near future. The world at

large appears to be tilted towards micro adventures called “Start-ups”. The course envisages imparting various

skill-sets – English communication, breading IP culture, creation of IP and related aspects, seeking funds,

management skills to handle stress to sustain business, sharing live experience from founders of start-ups.

Syllabus:

The Course aims to expose the students to the basic principles and practices in the broad field of “Start-up

ventures”: Communication in general and technical writing in particular; IP and IP protection; formalities of

registration, compliances, seeking funding and so on. It also aims to equip them with the knowledge; skills and

attitude they need to overcome problems in the creation & sustaining of Start-up. It is planned to invite founders

of Start-ups to relate their own experiences.

Target Group:

All entrepreneurs, students, engineers and scientists who aspires to kindle entrepreneur in her/him.

Faculty:

Dr. R.N. Narahari Centre for Nano Science

Engineering,


Email:, [email protected],

Faculty:

Prof SA

Shivashankar Centre for Nano Science

Engineering,


[email protected]

Reference Books:

1. The New Business Road Test: What Entrepreneurs

and Investors should do before Launching a Lean

Start-up; John Mullins, Pearson India Education

Services PL, Noida, 2019.

2. Entrepedia: A step-by-step guide to becoming an

entrepreneur in India by Prof Nandini Vaidyanatha,

Embassy Books.

3. Taraporevala, V J “Law of Intellectual Property”

Published by V J Taraporevala, Mumbai, 2005

4. The Elements of Style by William Strunk Jr & EB

White, Longman Publishers. Ma, USA 02494

Who can apply?

MSc in chemistry, M Pharm, MSc in

Physics




Schedule: Saturday’s 10.00 pm. to 1.00 pm

mailto:[email protected],shivu%[email protected]

Page 25 of 36

17. Online Course on Structural Analysis and Design Optimization:

Theory and Practice (2:0)

Objectives:

Advanced research in material science to enhance the life with reduced cost resulted in metal alloys, plastics, composites and

nano materials. Structural design and optimization of components with unusual shapes became possible with current available

finite element software tools such as ANSYS, NISA, NASTRON, ABACUS, SYSNOISE, LSDYNA and MATLAB etc.

The fundamental knowledge of stress, strain, shear, torsion in relation to the structures and S-N curves in relation to the

material fatigue life becomes important. The interpretation of the FEM software output calls for the knowledge of analysis

and design optimization of mechanical systems. This course essentially trains engineers/scientists/entrepreneurs/instructors

in the industries/institutes to optimally design various mechanical systems and sub-systems for technically superior and

commercially viable value-added product and achieve “EMPOWER INDIA WITH SKILL AND Knowledge”

Syllabus:

Applied mechanics, Strength of materials, SFD, BMD, AFD, solid mechanics, concept of stress, strain and fatigue.

Constitutive laws. Mohr’s Circle, Engineering materials and their properties. Structural analysis concepts, tension,

compression, shear, torsion, coupled system, and S-N curves. Design of beams, torsion, compression members and fasteners.

Stability of structures. Composite materials and their importance in structural analysis design optimization. Principles of

optimization, formulation of objective function and design constraints, classification of optimization problem. Single and

multivariable optimization. Optimization with equality and inequality constraints. Optimal design of mechanical elements –

fasteners, springs, gears, bearings, belts, clutches, brakes, shafts and axles. Procedures for product design, development and

testing. Vibration of structures

Practical problem discussion with industrial products (optimization of passenger car sub systems for vibration and noise

reduction, Rail-coach-CBC couplers, Car door window regulator, satellite tracking antenna and DWR antenna design, Tractor

canopy, hydraulic crawler driller (drilling machine), Bike brake system, sluice valve design, failure analysis if piston drill

bit, thermally insulated box, IP turbine blade failure analysis, design analysis of super pump impeller, Structural design

aspects in power plants. Hydraulic jacks/Feed cylinder with intermediate supports, Industrial chimney design, optimization

of box culverts, metal-composite sprocket for bikes, design criteria for Van pump, Thermal analysis of heat exchangers, 6-

DOF force balance, pitch flexure, roll flexure design for wind tunnel model studies for aerodynamic derivatives of aerospace

vehicle and automobiles).

Target Group:

Mechanical, Civil, Aerospace, Automotive, Industrial Engineers, R & D Labs, Construction Technologists, New product

Design and Development Groups, Entrepreneurs and Engineering College Instructors. Professionals to pursue Postgraduate

and Higher Studies

Faculty:

Dr. S B Kandagal Principal Research Scientist,

Dept of AE,

IISc., Bengaluru.


Reference Books

1. Beer F P and Johnson, E.R,

“Vector Mechanics for Engineers- Statics and

Dynamics”, Tata-Mac Graw Hill, Sixth Edison, 2012.

2. Shigley, J.E and Mischke, C.R.,

“Mechanical Engineering Design”

Tata-Mac Graw Hill, sixth Edison, 2010.

3. Johnson Ray, C. ” Optimum Design of Mechanical Elements”,

Wiley, John & Sons, 2014.

Who Can apply?

BE, ME, MSc, AMIE, or equivalent



Online Classes using Microsoft Teams/Google Meet

Schedule: Saturday’s 12.00 pm. to 2.00 pm.


Page 26 of 36

18. Online Course on Foundations of Data Science and Machine Learning

(3:0)

Objectives:

The course provides an accessible, yet rigorous mathematical foundation in probability, statistics, linear algebra,

and calculus required to understand data science including analytics, machine learning and deep learning. Classes

involve hands-on problem solving along with concepts being introduced through examples from the viewpoint of

data science. At the end of the course, students would have gained enough knowledge to solve problems in

foundational mathematics topics and undertake further advanced courses in Data Science, Analytics and Machine

Learning with confidence.

Syllabus:

Probability and Statistics Module: Probability axioms; Conditional Probability; Bayes' Theorem; Independence;

Counting Problems; Discrete and Continuous Random Variables; Expectation; Iterated Expectation; Total Law

of Probability; Covariance; Correlation; Entropy; Mutual Information; Frequentist Inference; Bayesian Inference.

Calculus Module: Functions; Derivatives; Multivariate Calculus; Jacobian; Hessian.

Linear Algebra Module: Vectors; Matrices; Basis; Norms; Orthonormality; Linear System, Rank, and Solution;

Linear Transformation; Matrix Multiplication; Matrix Decomposition Factorization; Cholesky Decomposition;

Singular Value Decomposition; Eigen Decomposition.

For more details check: http://cds.iisc.ac.in/faculty/deepakns/

Target Group:

All data science aspirants from industry (e.g., IT, Internet Companies, Finance, Engineering etc), R&D institutions (e.g.,

ISRO, NAL, CPRI etc)

Faculty:

Dr. Deepak N Subramani Assistant Professor,

Dept. of Computational and Data Sciences,

IISc, Bengaluru.


Reference Books:

• Bertsekas, Dimitri P., and John N. Tsitsiklis.

Introduction to Probability. Vol. 1. Belmont, MA

Athena Scientific, 2002.

• Gibert Strang. Linear Algebra for

Everyone,Wellesley-Cambridge Press, 2020

• Notes and problem sets from the instructor

Who can apply?

B.Tech./M.Sc. or Equivalent

Pre-requisites:

High-School Mathematics




Schedule: Saturday’s (1.00 pm to 4.00 pm)

http://cds.iisc.ac.in/faculty/deepakns/


Page 27 of 36

19. Business Analytics with Management Science Models and Methods

(3:0)

Objectives:

To provide business practitioners and those who are interested in Business Analytics a selected set of Management Science

and Optimization Techniques along with the fundamental concepts, methods, and models for understanding prescriptive-

analytics and implementation these techniques in the era of Big Data.

Syllabus:

Introduction to Business Analytics, Linear/Integer/Non-Linear Optimization, Optimization of Network Models, Dynamic

Programming, Heuristic Programming, Goal Programming, Multi-Attribute Decision Making Methods, and Monte Carlo

Simulation. These are believed to be among the most popular Prescriptive Analytics tools to solve a majority of business

optimization problems, with case studies from Business, Industry, and Government (BIG) applications using

LINDO/LINGO/CPLEX optimization package.

Target Group:

Every Business, Industry and Government (BIG) organizations which has “Business Analytics’ group to address various

problems associated with Prescriptive Analytics, In addition, all Faculty and interested UG and PG Graduates in Engineering

and Post Graduate in Business Administration/Management, Operations Research, Computer Science, Computer

Applications, Mathematics, Statistics, Economics.

Faculty:

Dr. M Mathirajan

Chief Research Scientist,

Dept. of M S.,

Faculty of Engineering,

IISc, Bengaluru.

Email: [email protected]; [email protected]

Reference Books:

1. Wayne LWinston.Operations Research:

Applications and Algorithms (Latest Edition).

Duxbury Press. An Imprint of Wadsworth

Publishing Company, Belmont, California, USA.

2. Anderson, Sweeney and Williams.

An Introduction to Management Science:

Quantitative Approaches to Decision Making

(Latest Edition). South-Western College

Publishing

3. U Dinesh Kumar,

Business Analytics:The Science of Data-Driven

Decision Making Wiley India, 2017.

Who Can apply?

BE/B.Tech., ME/M.Tech. MBA, and Master in

Operations Research, Computer Science,

Computer Applications, Mathematics, Statistics,

and Economics.




Schedule: Sunday’s – (10.00 am. to 1.00 pm.)



Page 28 of 36

20. Online Course on Reinforcement and Deep Reinforcement

Learning (3+0)

Objectives:

To provide both a rigorous foundation in Reinforcement Learning through the various tools, techniques

and algorithms used as well as cover the state-of-the-art algorithms in Deep Reinforcement Learning.

Syllabus:

Introduction to Reinforcement Learning, Multi-armed bandits, Markov decision processes and

Dynamic Programming, Model-Free Methods: Monte-Carlo and Temporal Difference approaches, Q-

learning, SARSA, Double Q-learning, Function approximation methods: TD Learning, Deep Q-

Network, Policy Gradient Approaches, TRPO, PPO, Deep Deterministic Policy Gradients,

Asynchronous Actor-Critic Algorithms.

Target Group:

People from Industry, College Teachers, Research and Project Staff will benefit.

Faculty:

Prof. Shalabh Bhatnagar

Dept. of CSA, IISc.


Reference Books

1. R.Sutton and A.Barto,

Reinforcement Learning, MIT Press, 2'nd

Ed., 2018

2. D.Bertsekas,

Reinforcement Learning and Optimal

Control, Athena Scientific, 2019

3. Recent Research Papers

Who can apply?

B.Tech/MCA/M.Sc



Online Classes using Microsoft Teams/

Schedule: Saturday’s 1.00 pm to 4.00 pm


Page 29 of 36

21.Online Course on Mathematics for Machine Learning (3:0)

Objectives:

The goal is to provide a self-contained introduction to some of the core mathematics that comes up in machine

learning, namely, calculus, linear algebra, probability and optimization. The course is particularly targeted at

professionals who wish to hone their math skills and understanding of the basic concepts. We will start with

foundational topics and gradually build up to various models and algorithms used in machine learning. Problem

solving in the form of exercises and assignments will be involved; solutions will be provided.

Syllabus:

Fundamentals of multivariate calculus, linear algebra, probability and optimization; applications in machine

learning: empirical risk minimization and PAC learning, linear and nonlinear regression, clustering,

dimensionality reduction, classification, linear predictors, kernel methods, neural networks and deep learning.

Target Group:

All students, engineers, and scientists, who aspire to kindle the “entrepreneur in her/him.

Faculty:

Prof. Kunal Narayan Chaudhury

Associate Professor,

Dept. of Electrical Engineering

IISc., Bengaluru.


Reference Books

1. Mathematics for Machine Learning by M.P.

Deisenroth, A.A. Faisal and C.S. Ong, Cambridge

University Press, 2020.

2. Understanding Machine Learning by S. Shalev-

Shwartz and S. Ben-David,

Cambridge University Press, 2014.

3. Notes provided by the instructor.

Who Can apply?

B.E./B.Tech. /M.Sc. (Math/Physics)




Schedule: Saturday’s – 2.00 pm. to 5.00

pm.


Page 30 of 36

22. Online Course on Introduction to AI & Machine Learning (3:0)

Objectives:

• Introducing classical AI

• State Space Search

• Bayesian Inference

• Statistical Machine Learning

• Artificial Neural Network

• Deep Neural Network

• Image processing with DNN

• Case Studies from Autonomous Vehicle and Industry 4.0

Syllabus:

State Space Search Uncertainty Modelling Bayesian Inferencing Supervised Machine Learning - Neural

Network Clustering Cross Validation & Cluster Validation Markov Decision Process Deep Learning Basics of

Image Processing with DNN Variable Autoencoder, Generative Adversarial Network Case Studies

Target Group:

Industry practitioners, Lecturers, Early-stage researchers, scientists

Faculty:

Dr. Pradipta Biswas

Assistant Professor,

Dept. of CPDM, IISc.


Reference Books:

1. Russell S and Norvig P., A Modern Approach to

Artificial Intelligence Aaron Courville 2. Ian Goodfellow, and Yoshua Bengio, Deep Learning

Who can apply?

BE, B.Tech., MSc




Schedule: Saturday’s 1.00 pm to 4.00 pm


Page 31 of 36

23. Online Course on Fundamentals of Machine Learning (3:0)

Objective

To teach the basics of Machine learning, which is widely applied in almost all fields in practice.

Syllabus:

Introduction to Data Science; Review of Probability theory and Linear Algebra Classification and Regression,

Covariance and Correlation coefficient, Simple regression, Least Squares and Maximum Likelihood estimation

of parameters, residues, Hypothesis testing, ANOVA Multiple regression, Partial correlation coefficient,

Estimation of parameters, Gradient Descent and Stochastic Gradient Descent methods Classification, Linear

and nonlinear classifiers, multiple classes, Dimensionality reduction - Principal component analysis, Linear

Discriminant Analysis; Nearest Neighbour Classifiers- k Nearest Neighbor (kNN) rule, Irrelevant attributes and

scaling problems; Fundamentals of Artificial Neural Networks, Structure of an artificial neural network,

Weights and Biases, Activation function, Training a neural network -error and back propagation of error,

stochastic gradient descent for optimizing weights; Decision Trees- Concept of a decision tree as a classifier,

Entropy and Information Gain, Pruning; Naïve Bayes' classifier review of Baye's theorem, Independence

assumption and Naïve Bayes Classification; Hidden Markov models- Introduction to Markov process, Discrete

state Markov process, state transition and transition probability, Three problems of HMM; Clustering- k Mean

Clustering; Software development project using Python consisting of all the topics learnt.

Target Group:

Any Industries that deal with data analysis and management and academics interested in data science.

Faculty

Dr. Gopal Krishna

Sharma Fiserv India Pvt. Ltd.,

Bengaluru.


Faculty

Dr. Badarinath Ambati

Altair Engineering,

Bengaluru.


Faculty

Prof. Muddu

Sekhar Dept. of Civil

Engineering,

IISc., Bengaluru.


Reference Books

1. Introduction to Machine Learning - Ethem

Alpaydin,2ed, MIT Press,2010 (or later)

2. Probability & Statistics with Reliability

Queuing and Computer Science Applications -

Kishore S Trivedi, 2 ed, Wiley Student edition

3. Forecasting Methods and Applications -

Spyros Makridakis et el,3ed, 2005 (or later)

Who Can apply?

Any Engineering Degree with Mathematics




Schedule: Saturday’s (10.00 am to 1.00 pm)




Page 32 of 36

24. Online Course on Embedded Systems and IoT Sensors Applications

(2:0)

Objectives:

This course an Advanced Course as Design and Development of Embedded Systems using ARM Processors.

Show cases plenty of plat forms, based on many ARM venders and Sensors both Hardware and Software support.

Syllabus:

Familiarization to Open-Source Embedded Systems, ARM Architecture understanding the Evaluation kits both

Hardware, Software, Development Platforms like NXP, SILABS, STMICRO etc. Introduction to many IoT

Sensors, interfaces to ADC/DAC etc. Introduction to RF Modules (BT, LORANAN, WiFi, GPS etc).

Demonstration of IoT based projects.

Target Group:

Hardware Electronic Engineers.

Faculty:

Mr. M Krishna Kumar

(Retd.),

PRS., Dept. of ESE

(CEDT),

IISc.., Bengaluru

Email. [email protected]

Faculty:

Mr. S M

Narasimhan

Electronic

Design

Consultant,

Mysuru

Faculty:

Dr. Arulalan Rajan,

Formerly,

Assistant Prof.,

Dept. of E&C Engg.,

NITK, Surathkal..

Email:

[email protected]

m

Reference Books:

1. Jonathan W. Valvano

Embedded Systems: Real-time Interfacing to

ARM Cortex-M Microcontrollers.

Volume 2 Fourth Edition, July 2014.

2. Joseph Yiu

System-on-Chip Design with Arm Cortex-M

Processors.

3. Steve Furber

Arm System-On-Chip Architecture.

Who can apply?

BE/B.Tech/ AMIE or equivalent.

Pre- Requisites:

Basic Knowledge in Analog, Digital Electronics

and Microcontrollers.




Schedule: Saturday’s – 2.00 pm to 4.00 pm



Page 33 of 36

25. Online Course on Foundations of Intellectual Property Rights (IPRs) –

Legal and policy perspective (2:0)

Objectives:

The aim of this course is to introduce participants to the broad legal and policy perspective of Intellectual

Property Rights (IPRs) with a specific focus on the Indian IPR regime. Through the course, participants will

learn about different aspects of IP, their importance for business and society, and their protection/ enforcement

(in India and worldwide) through practical case examples. After completing this course, participants will be able

to understand different types of IP rights, appreciate the benefits and challenges, and understand the aspects of

enforcement in India and abroad

Syllabus:

Foundation of intellectual property – Historical reference, categorization & characteristics international

perspective; Patents – Introduction, economic impact, Patent Cooperation Treaty (PCT), benefits and

enforcement of patent rights; Copyrights – Rights protected by copyright, acquisition and transfer of copyright,

India; Trademarks –characteristics and protection of TM WIPO’s Madrid System and other treaties;

Geographical Indication (GI) – international convention and treaties, GI vs. TM, Laws of GI in India; Industrial

Design (ID) –Laws to protect ID in India, Worldwide protection of ID – The Hague system of WIPO, Case

examples and discussion; Civil society, public policy and IP – Social dimension of IP, IPR and public policy

(technology transfer on fair, reasonable and equitable terms, access of medicines and environment-friendly

technologies)

Target Group:

R&D organization or research labs, Research and teaching professionals, Entrepreneurs or small business owners,

Government officers

Faculty:

Prof. Anjula Gurtoo

Dept of MS,

IISc, Bengaluru


Faculty: Dr Akriti Jain

Post doc,

Dept of MS, IISc,

Bengaluru


Reference Books:

1. Reddy P, Chandrashekaran S. Create, copy, disrupt.

India's intellectual property dilemmas. Oxford

University Press India; 2017.

2. Halliburton M. India and the Patent Wars. Cornell

University Press; 2017 Nov 15.

3. Ahuja, V. K. Intellectual Property Rights in India.

LexisNexis Butterworths Wadhwa Nagpur; 2009

Who can apply?

Master’s degree in any discipline




Schedule: Saturday’s – 2.00 pm. to 4.00 pm



Page 34 of 36

26.Online Course on Machine Learning (3:0)

Objectives:

To introduce the registrants to both the Theoretical and Practical aspects of Machine Learning. This will be

useful to both Practitioners and early Researchers and Academicians.

To introduce the registrants to the both the Theoretical and Practical aspects of Machine Learning. This will be

useful to both Practitioners and early Researchers and Academicians.

Syllabus:

Introduction to Machine Learning (ML). Representation and Search. Learning Paradigms: Rote Learning, Learning by

Deduction, Learning by Analogy, Learning by Abduction, Learning by Induction.

Foundations of ML: Role of Linear Algebra in ML; Information Theory in ML, Logic in ML, Probability in ML; and

Graphs in ML.

Inductive Learning: Clustering, Supervised and Semi-Supervised Learning, Knowledge-Based Learning.

Deep Learning: Convolution Neural Nets, Recurrent Neural Nets.

Applications: Information Retrieval and Network Embedding in Social and Information Networks.

Target Group:

Industry/R&D Units/ Academic Institutions interested in Machine Learning and Artificial Intelligence.

Faculty:

Prof. M Narasimha Murty

Honorary Professor,

Dept. of CSA.,

IISc, Bengaluru.


Reference Books:

1. Tom Mitchell,

Machine Learning, Indian Edition, July 2017.

2. M N Murty and V Susheela Devi,

Introduction to Pattern Recognition and

Machine Learning, IISc Lecture Series Notes,

World-Scientific, IISc Press, 2015.

3. S. J. Russell and P. Norvig,

Artificial Intelligence: A Modern Approach,

Pearson, Delhi, 2016.

Who can apply?

BE/BTech in any branch of Engineering or MSc in

Mathematics/Physics/Statistics/Computer Science or

MCA.

Pre-requisites:

A good background of college level mathematics and

programming.




Schedule: Sunday’s – 10.00 am. to 1.00 pm


Page 35 of 36

Appendix ‘A’ PROFORMA

NAME OF THE COLLEGE

PROVISIONAL CERTIFICATE

This is to certify that Sri/ Smt. ………………………………. was a student of this college

studying in ……………………………*

Course ……………………………………………………….**

Branch during the Session …………………. to ……………………………………..…

He / She have Successfully Completed the course as prescribed by the ……….………...

…………………………………………………………………………………………......

University with regard to course of study, attendance, sessional requirements etc.

He / She has passed the final ……………………………..* examination held during

……………………… securing …………………..class as per the results announced by

the University. He / She will be awarded the …………………………. …..degree during

the next convocation of the university.

College Seal

Date: PRINCIPAL

*Appropriate course to be filled in (B.E., B.Tech., M.E., M.Tech., M.Sc., and M.Com.

MBBS. Etc.)

**Mention Civil, Electrical, Electronics, Chemistry, Biology, Etc.

Page 36 of 36

IMPORTANT DATES

Apply online on CCE portal

17th June 2021

Thursday

Receipts of application along

with fees (upto)

From

To

17th June 2021

25th July 2021

Thursday

Sunday

Classes Commence

From 02nd August 2021 Monday

Final Exams From 29th November 2021 Monday

To 04th December 2021 Saturday

CCE-PROFICIENCE

Coordinator, Indian Institute of Science,

Bangalore - 560 012

Phone: + 91 080 22932508


URL: www.cce.iisc.ac.in/proficience

Working Hours: Monday through Friday: 09.30 hrs. to 18.00 hrs.

Saturdays’: 10.00 hrs. to 16.00 hrs.


http://www.cce.iisc.ac.in/proficience

Documents

CCE -PROFICIENCE INDIAN INSTITUTE OF SCIENCE …