B.Tech in ARTIFICIAL INTELLIGENE Curriculum and Syllabus · B.Tech. in Artificial Intelligence Mission of the Department Mission Stmt - 1 To impart knowledge in cutting edge Computer

ACADEMIC CURRICULA

B.Tech in

ARTIFICIAL INTELLIGENE

Curriculum and Syllabus

Academic Year - 2020

SRM INSTITUTE OF SCIENCE AND TECHNOLOGY

(Deemed to be Univers ity u/s 3 of UGC Act , 1956)

Katta nk u lat h u r, Ka nc hee p u ram , Tam i l Nad u , I n d ia

SRM INSTITUTE OF SCIENCE AND TECHNOLOGY

Kattankulathur, Kancheepuram District 603203, Tamil Nadu, India

B.Tech. in Artificial Intelligence Mission of the Department

Mission Stmt - 1 To impart knowledge in cutting edge Computer Science and Engineering technologies in par with industrial standards.

Mission Stmt - 2 To collaborate with renowned academic institutions to uplift innovative research and development in Computer Science and Engineering and its allied fields to serve the needs of society

Mission Stmt - 3 To demonstrate strong communication skills and possess the ability to design computing systems individually as well as part of a multidisciplinary teams.

Mission Stmt - 4 To instill societal, safety, cultural, environmental, and ethical responsibilities in all professional activities

Mission Stmt - 5 To produce successful Computer Science and Engineering graduates with personal and professional responsibilities and commitment to lifelong learning

Program Educational Objectives (PEO)

PEO - 1 Graduates will be able to perform in technical/managerial roles ranging from design, development, problem solving to production support in software industries and R&D sectors.

PEO - 2 Graduates will be able to successfully pursue higher education in reputed institutions.

PEO - 3 Graduates will have the ability to adapt, contribute and innovate new technologies and systems in the key domains of Computer Science and Engineering.

PEO - 4 Graduates will be ethically and socially responsible solution providers and entrepreneurs in Computer Science and other engineering disciplines.

PEO - 5 Graduates will be able to explore recent technological developments related to Systems Engineering.

PEO - 6 Graduates will have the ability to explore research areas and produce outstanding contribution in various areas of Systems Engineering.

Mission of the Department to Program Educational Objectives (PEO) Mapping

Mission Stmt. - 1 Mission Stmt. - 2 Mission Stmt. - 3 Mission Stmt. - 4 Mission Stmt. - 5

PEO - 1 H H H H H

PEO - 2 L H H H H

PEO - 3 H H M L H

PEO - 4 M H M H H

PEO - 5 H H M M H

PEO - 6 M H H H H

H – High Correlation, M – Medium Correlation, L – Low Correlation

Mapping Program Educational Objectives (PEO) to Program Learning Outcomes (PLO)

Program Learning Outcomes (PLO)

Graduate Attributes (GA) Program Specific Outcomes

(PSO)

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PEO - 1 H H H H H H H H H H H H H H H

PEO - 2 H H H H H L L H L H L H H H H

PEO - 3 H H H H H L L L L L H H H H H

PEO - 4 H H H H H H H H H H H H H H H

PEO - 5 H L L H H L L L L L H H H H H

PEO - 6 L H H H H H H L L L L L H H L

H – High Correlation, M – Medium Correlation, L – Low Correlation

PSO – Program Specific Outcomes (PSO) PSO - 1 Ability to learn Artificial intelligence and related Principles

PSO - 2 Ability to Create new Techniques and develop algorithms for Artificial Intelligence

PSO - 3 Ability to Develop systems using techniques and tools relating to Artificial Intelligence

Program Structure: B.Tech. in Artificial Intelligence

1. Humanities & Social Sciences including Management Courses (H)

Course Course Hours/ Week

Code Title L T P C 18LEH101J English 2 0 2 3 18LEH102J Chinese

18LEH103J French

18LEH104J German 2 0 2 3

18LEH105J Japanese

18LEH106J Korean

18PDH101T General Aptitude 0 0 2 1

18AIH201T Management Principles for Data Analysis 2 0 0 2

18AIH202T Ethics and Policy Issues in AI Computing 2 0 0 2

18PDH201T Employability Skills & Practices 0 0 2 1

Total Learning Credits 12

2. Basic Science Courses (B)


Code Title L T P C 18PCB101J Engineering Physics ,Chemistry and Biology 3 1 2 5 18AIB101J Foundation of Data Analysis 3 1 2 5

18MAB101T Calculus and Linear Algebra 3 1 0 4

18MAB102T Advanced Calculus and Complex Analysis 3 1 0 4

18MAB201T Transforms and Boundary Value Problems 3 1 0 4

18AIB102T Probability for Machine Learning 3 1 0 4

18MAB302T Discrete Mathematics for Engineers 3 1 0 4

18AIB201T Convex Optimization and Applications 2 0 0 2


4. Professional Core Courses (C)


Code Title L T P C

18CSC201J Data Structures and Algorithms 3 0 2 4

18AIC201J Application Based Programming using Python 3 0 2 4

18AIC202J Foundation of Artificial Intelligence and Machine Learning

3 0 2 4

18CSC204J Design and Analysis of Algorithms 3 0 2 4

18CSC205J Operating Systems 3 0 2 4

18CSC206J Software Engineering and Project Management 3 0 2 4

18AIC203J Data Analytics 3 0 2 4

18CSC301T Formal Language and Automata 3 0 0 3

18CSC302J Computer Networks 3 0 2 4

18CSC303J Database Management Systems 3 0 2 4 18CSC304J Compiler Design 3 0 2 4

18AIC301J Neural Networks and Deep Learning 3 0 2 4

18CSC350T Comprehension 0 1 0 1

18CSC208L Competitive Professional Skills - I 0 0 2 1

18CSC306L Competitive Professional Skills – II 0 0 2 1

18CSC307L Competitive Professional Skills - III 0 0 2 1


3. Engineering Science Courses (S)


Code Title L T P C

18AIS101J Introduction to MATLAB for Artificial Intelligence 1 0 4 3

18EES101J Basic Electrical and Electronics Engineering 3 1 2 5

18AIS102J Smart Manufacturing 1 0 4 3

18CSS101J Programming for Problem Solving 3 0 4 5

18CSS201J Analog and Digital Electronics 3 0 2 4

18CSS202J Computer Communications 2 0 2 3


5. Professional Elective Courses (E)

(Any 6 Elective Courses)


Code Title L T P C

18AIE321T Logic and Knowledge Representation 3 0 0 3

18AIE322T Numerical Mathematics for Data Science 3 0 0 3

18AIE323T Machine Learning Optimization Algorithms 3 0 0 3

18AIE324T Big Data Frameworks – Hadoop, Spark and NoSQL

3 0 0 3

18AIE325T Deep Learning: Theory and Practice 3 0 0 3

18AIE326T Graph Analytics for Big Data 3 0 0 3

18AIE327T Complex Network Analysis and Visualization 3 0 0 3

18AIE328T Bioinformatics 3 0 0 3

18AIE329T Robotics and Intelligent Systems 3 0 0 3

18AIE330T Stochastic Decision Making 3 0 0 3

18AIE331T Building and Mining Knowledge Graphs 3 0 0 3

18AIE332T Pattern Recognition Algorithms 3 0 0 3

18CSE359T Natural Language Processing 3 0 0 3

18AIE421T Intelligent Autonomous Systems 3 0 0 3

18AIE422T Speech Processing 3 0 0 3

18AIE423T Design of Artificial Intelligence Products 3 0 0 3

18AIE424T Reinforcement Learning 3 0 0 3

18AIE425T Data Privacy by Design 3 0 0 3


6. Open Elective Courses (O) (Any 3 Open Elective Courses)


Code Title L T P C

Vertical Open Elective - 1 3 0 0 3

Vertical Open Elective – 2 3 0 0 3

Vertical Open Elective -3 3 0 0 3

Students need to choose any 3 subjects from anyone vertical. 1. Smart Healthcare 2. Robotics 3. Business Analytics 4. Infrastructure 5. Cyber Security and Intelligence 6. Agriculture


7. Project Work, Seminar, Internship In Industry/ Higher Technical Institutions (P)


Code Title L T P C

18AIP101L MOOC / Industrial Training / Seminar - 1 0 0 2 1


18AIP103L Project (Phase-I) / Internship (4-6 weeks) 0 0 6 3

8. Mandatory Courses (M)

Code Course Title L T P C

18PDM101L Professional Skills and Practices 0 0 2 0

18PDM201L Competencies in Social Skills 0 0 2 0

18PDM203L Entrepreneurial Skill Development

18PDM202L Critical and Creative Thinking Skills 0 0 2 0

18PDM204L Business Basics for Entrepreneurs

18PDM301L Analytical and Logical Thinking Skills 0 0 2 0

19PDM302L Entrepreneurship Management

18LEM101T Constitution of India 1 0 0 0

18LEM102J Value Education 1 0 1 0

18GNM101L Physical and Mental Health using Yoga 0 0 2 0

18AIP104L Project (Phase-II) / Semester Internship 0 0 20 10


8. Mandatory Courses (M)


Code Title L T P C 18GNM102L NSS

0 0 2 0 18GNM103L NCC

18GNM104L NSO

18LEM109T Indian Traditional Knowledge 1 0 0 0

18LEM110L Indian Art Form 0 0 2 0

18CYM101T Environmental Science 1 0 0 0

Program Articulation:B.Tech in Artificial Intelligene

Program Learning Outcomes (PLO)

Graduate Attributes PSO

Course Code

Course Name

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18CSC201J Data Structures and Algorithms H H H H M L L M H M M H L H H 18CSC204J Design and Analysis of Algorithms H H H H M M L M M M M H L H H

18CSC205J Operating Systems H H H H H M L M H M M H H H M

18CSC206J Software Engineering and Project Management H H H H H H H H H H H H L H M

18CSC301T Formal Language and Automata H H H H L L L L M M L H H H H

18CSC302J Computer Networks H H H H H M L M H M M H H H M

18CSC303J Database Management Systems H H H H H M L M H M M H H H M

18CSC304J Compiler Design H H H H M L L L M M L H H H H

18AIH201T Management Principles for Data Analysis M M M M H H H H H H H H H H H

18AIH202T Ethics and Policy Issues in AI Computing M M M M L H H H M H H H H H H

18AIB101J Foundation of Data Analysis H H H H H M M L M M M H H H H

18AIB102T Probability for Machine Learning H H H H H M M L M M M H H H H

18AIB201T Convex Optimization and Applications H H H H H M M L M M M H H H H 18AIS101J Introduction to MATLAB for Artificial Intelligence H H H H H M M L M M M H H H H

18AIS102J Smart Manufacturing H H H H H H H H H H H H H H H

18AIC201J Application Based Programming using Python H H H H H M M L M M M H H H H

18AIC202J Foundation of Artificial Intelligence and Machine Learning H H H H H M M L M M M H H H H

18AIC203J Data Analytics H H H H H M M L M M M H H H H

18AIC301J Neural Networks and Deep Learning H H H H H M M L M M M H H H H

18CSC208L Competitive Professional Skills – I H H H H H L L M H H M H H H H

18CSC306L Competitive Professional Skills - II H H H H H L L M H H M H H H H

18CSC307L Competitive Professional Skills - III H H H H H L L M H H M H H H H

18AIE321T Logic and Knowledge Representation H H H H H M M L M M M H H H H

18AIE322T Numerical Mathematics for Data Science H H H H H M M L M M M H H H H

18AIE323T Machine Learning Optimization Algorithms H H H H H M M L M M M H H H H

18AIE324T Big Data Frameworks – Hadoop, Spark and NoSQL H H H H H M M L M M M H H H H

18AIE325T Deep Learning: Theory and Practice H H H H H M M L M M M H H H H

18AIE326T Graph Analytics for Big Data H H H H H M M L M M M H H H H 18AIE327T Complex Network Analysis and Visualization H H H H H M M L M M M H H H H

18AIE328T Bioinformatics H H H H H M M L M M M H H H H

18AIE329T Robotics and Intelligent Systems H H H H H M M L M M M H H H H

18AIE330T Stochastic Decision Making H H H H H M M L M M M H H H H

18AIE331T Building and Mining Knowledge Graphs H H H H H M M L M M M H H H H

18AIE332T Pattern Recognition Algorithms H H H H H M M L M M M H H H H

18AIE421T Intelligent Autonomous Systems H H H H H M M L M M M H H H H

18AIE422T Speech Processing H H H H H M M L M M M H H H H

18AIE423T Design of Artificial Intelligence Products H H H H H M M L M M M H H H H

18AIR424T Reinforcement Learning H H H H H M M L M M M H H H H

18AIE425T Data Privacy by Design H H H H H M M L M M M H H H H

18CSE359T Natural Language Processing H H H H H M M L M M M H H H H

18CSP101L MOOC / Industrial Training / Seminar - 1 H M M M M M M M H H H M H H H

18CSP102L MOOC / Industrial Training / Seminar - 2 H M M M M M M M H H H M H H H

18CSP103L Project (Phase-I) / Internship (4-6 weeks) H H H H H M M H H H H H H M M

18CSP104L Project (Phase-II) / Semester Internship H H H H H M M H H H H H H M M

Program Average H H M H M L M L M M M H M M M

Implementation Plan: B.Tech in Artificial Intelligene

Semester - I

Code Course Title Hours/ Week

C L T P

18LEH101J English 2 0 2 3

18MAB101T Calculus and Linear Algebra 3 1 0 4 18PCB101J Engineering Physics,Chemistry and Biology 3 1 2 5

18AIS101J Introduction to MATLAB for Artificial Intelligence 1 0 4 3

18EES101J Basic Electrical and Electronics Engineering 3 1 2 5

18PDM101L Professional Skills and Practices 0 0 2 0

18LEM101T Constitution of India 1 0 0 0

18GNM101L Physical and Mental Health using Yoga 0 0 2 0


Semester - II


C L T P

18LEH10XJ Chinese / French / German / Japanese/ Korean 2 0 2 3

18MAB102T Advanced Calculus and Complex Analysis 3 1 0 4 18AIB101J Foundation of Data Analysis 3 1 2 5

18CSS101J Programming for Problem Solving 3 0 4 5

18AIS102J Smart Manufacturing 1 0 4 3

18PDH101T General Aptitude 0 0 2 1

18LEM102J Value Education 1 0 1 0

18GNM10XL NCC / NSS / NSO 0 0 2 0


Semester - III


C L T P 18MAB201T Transforms and Boundary Value Problems 3 1 0 4

18AIB201T Convex Optimization and Application 2 0 0 2

18CSS201J Analog and Digital Electronics 3 0 2 4 18CSC201J Data Structures and Algorithms 3 0 2 4

18AIC201J Application Based Programming using Python 3 0 2 4

18AIC202J Foundation of Artificial Intelligence and Machine Learning

3 0 2 4

18AIH201T Management Principles for Data Analysis 2 0 0 2

18PDM201L Competencies in Social Skills 0 0 2 0

18PDM203L Entrepreneurial Skill Development


Semester - IV


C L T P 18AIB102T Probability for Machine Learning 3 1 0 4

18CSS202J Computer Communications 2 0 2 3

18CSC204J Design and Analysis of Algorithms 3 0 2 4 18CSC205J Operating Systems 3 0 2 4

18CSC206J Software Engineering and Project Management 3 0 2 4

18AIC203J Data Analytics 3 0 2 4

18CSC208L Competitive Professional Skills - I 0 0 2 1

18AIH202T Ethics and Policy Issues in AI Computing 2 0 0 2

18PDM202L Critical and Creative Thinking Skills 0 0 2 0

18PDM204L Business Basics for Entrepreneurs

18CYM101T Environmental Science 1 0 0 0


Semester - V


C L T P

18MAB302T Discrete Mathematics for Engineers 3 1 0 4

18CSC301T Formal Language and Automata 3 0 0 3

18CSC302J Computer Networks 3 0 2 4

18CSC306L Competitive Professional Skills - II 0 0 2 1

Professional Elective – 1 3 0 0 3


Open Elective – 1 3 0 0 3


18PDM301L Analytical and Logical Thinking Skills 0 0 2 0

19PDM302L Entrepreneurship Management 18LEM109T Indian Traditional Knowledge 1 0 0 0


Semester - VI


C L T P

18CSC303J Database Management Systems 3 0 2 4

18CSC304J Compiler Design 3 0 2 4

18AIC301J Neural Networks and Deep Learning 3 0 2 4

18CSC307L Competitive Professional Skills - III 0 0 2 1

18CSC350T Comprehension 0 1 0 1





18PDH201T Employability Skills and Practices 0 0 2 1 18LEM110L Indian Art Form 0 0 2 0


Semester - VII


C L T P




18AIP103L Project (Phase-I) / Internship (4-6 weeks) 0 0 6 3


Semester - VIII


C L T P

18AIP104L Project (Phase-II) / Semester Internship 0 0 20 10


Course Code

18PCB101J Course Name

ENGINEERING PHYSICS, CHEMISTRY AND BIOLOGY Course Category

B Basic Sciences L T P C

3 1 2 5

Pre-requisite Courses

Nil Co-requisite Courses

Nil Progressive Courses

Nil

Course Offering Department Physics and Nanotechnology Data Book / Codes/Standards Nil

Course Learning Rationale (CLR):

The purpose of learning this course is to: Learning Program Learning Outcomes (PLO)

CLR-1 : Provide specialized knowledge in the area of physics, chemistry and biology and to apply the knowledge in Artificial Intelligence

1 2 3 1 2 3 4 5 6 7 8 9 10

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CLR-2 : Provide fundamental principles of physics, chemistry and biology

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CLR-3 : Understand the importance of Artificial Intelligence in future technologies

CLR-4 : Application of Artificial Intelligence systems in operations

CLR-5 : Design of Integrated processes

CLR-6 : Aware of cyber regulations, laws and auditing pertaining to integrate systems

Course Learning Outcomes (CLO):

At the end of this course, learners will be able to:

CLO-1 : Familiar with the common unit operations encountered in various systems. 2 85 75 M H H H H H H H H H H H H H H

CLO-2 : Create an appropriate Artificial Intelligence integrated system. 2 75 70 H H H H H H M H H M H H H H H

CLO-3 : Propose an AI integrated system to achieve a specific end use. 2 80 75 H H M H H H H H H H H M H H H

CLO-4 : Recognize and discuss emerging technologies for advanced operation systems. 2 75 70 M H H H H H H M H M H H H H H

CLO-5 : Discuss AI integration in different industrial operations. 2 80 70 H H H M H H H H M H H H H H H

CLO-6 : Implicate the laws and regulatory concerns into research on novel technologies. 2 80 70 H H H H H H H H H H H H H H H

Duration (hour) Physics Physics Chemistry Biology Biology

S-1 SLO-1 Introduction to physics and its role in artificial intelligence

Introduction to Electrodynamics

Basic Chemistry Introduction to Biology B-cell and T-cell

SLO-2 Physics, Technology and Society

Maxwell’s equations Basic Stoichiometry Taxonomy, classification and systematics

Immunity – Active and passive

S-2 SLO-1 Scope of Physics- Macroscopic domain

Electrostatics Quantum Mechanical Model of Atom

Microbiology – Introduction Immune Memory

SLO-2 Microscopic domain Magnetostatics Periodic table and it’s Classification

Infections – Acute/Chronic, Treatment and cure

Monoclonal Antibody

S-3 SLO-1 Mechanics- Kinematics Electromagnetic induction Chemical Bonds- Ionic, Covalent, Hydrogen and Vandervall’s force

Cholera- disease, pathogen, mechanism and treatment

murine antibody

SLO-2 Dynamics Electromagnetic waves Physical – Chemical Equilibrium Application – Industrial, pharmaceutical, agricultural, beverage, energy.

recombinant antibody production

S-4 SLO-1 Use of mechanics in AI - Desalinating water with energy efficient materials

Electromagnetic theory Solid, Liquid and gas states Animal Biology – Introduction antigen target antibody production

SLO-2 Inspecting infrastructure with automated unmanned aerial vehicles

Implications for artificial intelligence

Derivation of all states Types of breeding. Vaccine shots – Dosage, timeline, types of vaccine shots-BCG, Tetanus Toxide, etc.…

S 5-6

SLO-1 Thermodynamics- Classical Thermodynamics- Chemical Thermodynamics- Equilibrium Thermodynamics

Quantum Brain dynamics Stoichiometry of all states Artificial insemination Environmental Biotechnology - Introduction

SLO-2 Fluid Mechanics - Hydrodynamics - Aerodynamics

Elementary radiation theory. Fundamentals of Inorganic Chemistry

super ovulation Origin of wastes, natural degradation process

S-7 SLO-1 Applications of Fluid dynamics in AI - Modelling combustion systems with Artificial intelligence

Electric current -Steady and varying

Stoichiometry of Inorganic Chemistry

Conservation of endangered sp., cryopreservation.

Types of waste generation from municipality and industries-Liquid, Solid, Gas

SLO-2 Prediction and prevention of extreme events

Alternating currents Fundamentals of Organic Chemistry

Cloning- Dolly (Goat) Recovery, reuse and disposal of waste

S-8 SLO-1 Introduction to Optics- Diffraction and optical resolution

Application of AI in electrical automation -Collision prevention; sensors Parking sensors

Stoichiometry in organic Chemistry

Transgenic fish and mice Aerobic, Anaerobic and Anoxic Treatment Methods and technologies

SLO-2 Dispersion and scattering; Polarization

Automatic wind screen wipers; Driverless cars

Introduction to Hydrocarbons Vaccines- Introduction, Dosage and Timeline

Solid waste Management-Landfilling, Composting, incineration

S-9 SLO-1 Optics- Introduction Introduction to Quantum Mechanics- Planck’s radiation law; Einstein’s photoelectric effect

Classification -Long Chain, Short Chain

Types - Killed, live, attenuated, conjugate, subunit, recombinant, DNA/RNA vaccines.

CO2 sequestration

SLO-2 Laser, Fiber optics, X-rays Bohr’s theory of the atom; Scattering of X-rays

Alkanes, Alkenes and Alkynes Application- Probiotics and vaccines (Commercial farm improvement)

Alternative energy – Biofuel, Biogas, Hydrogen fuel cells

S-10 SLO-1 Role of Optics in AI hardware De Broglie’s wave hypothesis Alkanes, Alkenes and Alkyne - stoichiometry

Animal cells in bioreactors- recombinant protein production, enzyme production

Bio Pesticides, Bio Insecticides, Bio fertilizers,

SLO-2 Ultrasonic Transducers Schrödinger’s wavemechanics Thermodynamics – Introduction Botany – Introduction to Plants Bioremediation and Bioagumentation

S 11-12

SLO-1 The Doppler effect Electron spin and anti-particles; Tunnelling

Derivations of Thermodynamics Callus formation Genetically Modified Organisms - Introduction

SLO-2 AI Applications using Ultrasonic- Disease Diagnosis; Therapy and Surgery

Axiomatic approach and Incompatible observables

Stoichiometry of Thermodynamics

Pluripotency and Totipotency GMO – Industrial, medical, food and agricultural applications

S-13 SLO-1 Material Science- Machine learning Heisenberg’s uncertainty principle

Electro Chemistry – Introduction types of breeding, hybridization, crop improvement

Bt Brinjal, Bt Cotton – Bacillus thirungenesis.

SLO-2 Prediction of material properties Derivation Derivation and Problems Biotic and Abiotic stresses; Resistant plants

Genetically Enhanced plants – Golden Rice, Leguminous plants

S-14 SLO-1 Novel materials for the fabrication of Artificial intelligence networks

Quantum electrodynamics Real time applications- Electroplating, Battery, Electro dusting, etc…

Resistance – Draught, flood, heat, salt, etc.… and Mechanisms

Entomogy – Introduction

SLO-2 Astrophysics- Gravitational wave signatures

Derivation and problems Applied Chemistry – Hazardous waste disposal and control;

Plant-Secondary metabolite Basics, X -Ray, CT Scans, Ultrasound

S-15 SLO-1 Bending of light due to gravity Electron- wave or particle Thermochemistry – Polymers and Composites

Products from plants-Drug, Insecticides and Rodenticides

3D Printing – Artificial heart, bones, joints, hips.

SLO-2 AI- design of convolution of neural networks capable of analyzing images

hidden variables Advance Chemistry –, Nanomaterial, Nanoparticles.

Immunology- Introduction Advanced Médical devices and Equipements

S-16 SLO-1 Atmospheric Physics and AI- Weather forecasts

Paradox of Einstein Sensors and Biosensors Somatic and Stem cells Software development – Algorithms for efficient performance

SLO-2 Self-organizing maps Podolsky and Rosen Integrated Biology- drug delivery systems

Gene therapy – Germline and Somatic

AI Neural Networking- An Introduction, Histograms

S 17-18

SLO-1 Nuclear Physics Development of artificial neural networks and support vector machines; Neural networks in identifying electrons and in determining heavy quarks.

Measurement in quantum mechanics

Agrochemistry, Geo chemistry Medicinal chemistry,

Antigens and Antibodies AI integrated Biology – New innovations of Medical Biotechnology

SLO-2 AI Research Scope- Intelligent information retrieval algorithms; Self-driving cars; Robots;Drones; Autonomous devices and systems

Applications of Quantum Mechanics- Decay of the koan, Cesium clock, A quantum voltage standard.

Renewable and Non-Renewable sources – Conservation of energy

Immunity glands and their functions Humanoid AI – A new era of Human integrated AI systems

Learning Resources

Learning Assessment

Bloom’s Level of Thinking

Continuous Learning Assessment (50% weightage) Final Examination (50% weightage)

CLA – 1 (10%) CLA – 2 (15%) CLA – 3 (15%) CLA – 4 (10%)#

Theory Practice Theory Practice Theory Practice Theory Practice Theory Practice

Level 1 Remember 20% 20% 15% 15% 15% 15% 15% 15% 15% 15%

Understand

Level 2 Apply 20% 20% 20% 20% 20% 20% 20% 20% 20% 20%

Analyze

Level 3 Evaluate 10% 10% 15% 15% 15% 15% 15% 15% 15% 15%

Create

Total 100 % 100 % 100 % 100 % 100 %

# CLA – 4 can be from any combination of these: Assignments, Seminars, Tech Talks, Mini-Projects, Case-Studies, Self-Study, MOOCs, Certifications, Conf. Paper etc.,

Course Designers

Experts from Industry Experts from Higher Technical Institutions Internal Experts

Course Code

18AIB101J Course Name

FOUNDATION OF DATA ANALYSIS Course Category

B Basic Sciences L T P C

3 1 2 5




Nil

Course Offering Department Data Book / Codes/Standards Periodic Table



CLR-1 : Introduce a range of topics and concepts related to the data analysis process. 1 2 3 1 2 3 4 5 6 7 8 9 10

11

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CLR-2 : Understand the basic mathematical models for large data sets.

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PS

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PS

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CLR-3 : Understand the principles and purposes of data analytics, and articulate the different dimensions of the area.

CLR-4 : Understand the principles and purposes of data analysis

CLR-5 : Apply basic data mining machine learning techniques to build a classifier or regression model

CLR-6 : provide exposure to theory as well as practical systems and software used in data analysis



CLO-1 : Obtain, clean/process, and transform data 2 70

65

CLO-2 : select appropriate model for data analysis 2 80

70

CLO-3 : Use appropriate models of analysis, 2 75

60

CLO-4 : assess the quality of input, derive insight from results, and investigate potential issues 2 70

70

CLO-5 : Apply computing theory, languages, and algorithms, as well as mathematical and statistical models, and the principles of optimization to appropriately formulate and use data analyses

2 80

70

CLO-6 : Formulate and use appropriate models of data analysis to solve hidden solutions to business-related challenges 2 75

65

Duration (hour) 18 18 18 18 18

S-1 SLO-1 The Learning Problem Training versus Testing The Linear Model Overfitting Probability Review

SLO-2 Problem Setup Theory of Generalization Linear Classification Overftting with Polynomials Sample Spaces

S-2 SLO-1 Components of Learning Effective Number of Hypotheses Non-Separable Data Catalysts for Overfitting Conditional Probability and Independence

SLO-2 A Simple Learning Model Bounding the Growth Function Non-Separable Data Catalysts for Overfitting Density Functions

S-3 SLO-1 Learning versus Design The VC Dimension Linear Regression Regularization Joint, Marginal, and Conditional Distributions

SLO-2 Learning versus Design The VC Generalization Bound Linear Regression Regularization Joint, Marginal, and Conditional Distributions

S-4 SLO-1 Tutorial Session Tutorial Session Tutorial Session Tutorial Session Tutorial Session

SLO-2 Tutorial Session Tutorial Session Tutorial Session Tutorial Session Tutorial Session

S 5-6

SLO-1 A Learning puzzle A Learning puzzle A Learning puzzle A Learning puzzle A Learning puzzle

SLO-2

S-7 SLO-1 Types of Learning Interpreting the Generalization Bound

Logistic Regression Validation Convergence and Sampling

SLO-2 Supervised Learning Sample Complexity Logistic Regression The Validation Set Convergence and Sampling

S-8 SLO-1 Reinforcement Learning Penalty for Model Complexity Predicting a Probability Model Selection Sampling and Estimation

SLO-2 Reinforcement Learning Penalty for Model Complexity Predicting a Probability Model Selection Sampling and Estimation

S-9 SLO-1 Unsupervised Learning The Test Set Gradient Descent Cross Validation Probably Approximately Correct (PAC)

SLO-2 Unsupervised Learning Other Target Types Gradient Descent Cross Validation Probably Approximately Correct (PAC)



S 11-12

SLO-1 A Learning puzzle A Learning puzzle A Learning puzzle A Learning puzzle Problem solving

SLO-2

S-13 SLO-1 Is Learning Feasible? Approximation-Generalization Tradeoff

Nonlinear Transformation Three Learning Principles Linear Algebra Review

SLO-2 Outside the Data Set Approximation-Generalization Tradeoff

Nonlinear Transformation Occam's Razor Vectors and Matrices

S-14 SLO-1 Probability to the Rescue Bias and Varian e The Z Spa e Sampling Bias Addition and Multiplication

SLO-2 Feasibility of Learning Bias and Varian e The Z Spa e Sampling Bias Linear Independence

S-15 SLO-1 Error Measures The Learning Curve Computation and Generalization Data Snooping Rank

SLO-2 Noisy Targets The Learning Curve Computation and Generalization Data Snooping Inverse



S 17-18

SLO-1 A Learning puzzle A Learning puzzle A Learning puzzle A Learning puzzle Problem solving

SLO-2

Learning Resources

1. Yaser S. Abu-Mostafa, Malik Magdon-Ismail, Hsuan-Tien Lin, Learning From Data, AMLBook

2. Jeff M Phillips, Mathematical Foundations for Data Analysis, Mathfor Data, December 2019

Learning Assessment


Continuous Learning Assessment (50% weightage) Final Examination (50% weightage) CLA – 1 (10%) CLA – 2 (15%) CLA – 3 (15%) CLA – 4 (10%)#


Level 1 Remember 20% 20% 15% 15% 15% 15% 15% 15% 15% 15%

Understand

Level 2 Apply 20% 20% 20% 20% 20% 20% 20% 20% 20% 20%

Analyze

Level 3 Evaluate 10% 10% 15% 15% 15% 15% 15% 15% 15% 15%

Create

Total 100 % 100 % 100 % 100 % 100 %


Course Designers


Course Code

18AIS101J Course Name

INTRODUCTION TO MATLAB FOR ARTIFICIAL INTELLIGENCE

Course Category

S Engineering Sciences L T P C

1 0 4 3

Pre-requisite Courses Nil Co-requisite Courses Nil Progressive Courses Nil

Course Offering Department Data Book / Codes/Standards Nil

Course Learning Rationale (CLR): The purpose of learning this course is to: Learning Program Learning Outcomes (PLO)

It is designed to give students fluency in MATLAB 1 2 3 1 2 3 4 5 6 7 8 9 10 11 12 13

14 15

CLR-1 : Students will be able to learn the different functionalities of MATLAB

Leve

l of T

hink

ing

(Blo

om)

Exp

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blem

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evel

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Res

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Mod

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Too

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Soc

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& C

ultu

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Env

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&

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Eth

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Indi

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PS

O -

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PS

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PS

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CLR-2 : Students will be able to do plotting

CLR-3 : Design and implement MATLAB code to solve small-scale scientific and engineering problems CLR-4 : Understand basic numerical method techniques for solving non-linear equations.

CLR-5 : Design and implement MATLAB code for classifier algorithm

Course Learning Outcomes (CLO): At the end of this course, learners will be able to:

CLO-1 : Locate, understand, and use a wide range of pre-defined functions. 3 90 85 CLO-2 : Select and use appropriate scalar and aggregate data types. 2 95 90

CLO-3 : Select and use appropriate control structures. 3 90 85

CLO-4 : Select and use appropriate input/output operations for terminal, file, graphical, and GUI-based input/output 3 90 85

CLO-5 : Understand basic numerical method techniques for solving non-linear equations in MATLAB 3 85 80

CLO-6 : Implement classifier machine learning algorithms in MATLAB 2 90 85

Duration

(hour) 15 15 15 15 15

S-1 SLO-1 MATLAB introduction Functions Linear algebra Introduction to machine learning GUI

SLO-2 Basics User defined functions System of linear equations

S-2 SLO-1 Scripts Function overloading Matrix decomposition Supervised learning Making the GUI

SLO-2 Writing scripts Function overloading Matrix decomposition

S-3 SLO-1 Variables Relational operators polynomials Classification Draw the GUI

SLO-2 Naming Conditions

S-4 SLO-1 Scalars Looping statements Polynomial operations Decision tree classifier Change the settings

SLO-2 Arrays Looping statements

S-5 SLO-1 Row vectors Plot options Polynomial fitting

Create a decision tree for the iris data and see how well it classifies the irises into species.

Save

SLO-2 Column vectors, size and length Plot options

S-6

SLO-1 Matrices Line and market options Non linear root finding visualize the regions assigned to each species

Adding functionalities to M file

SLO-2 Basic scalar operations Line and market options

S-7 SLO-1

Built in functions, transpose, addition and subtraction

Cartesian plots Minimizing a function draw a diagram of the decision rule and class assignments.

Running

SLO-2 Element wise functions Cartesian plots Anonymous

S-8 SLO-1 Operators 3D plots Optimization tool box Compute the resubstitution error and the cross-validation error for decision tree.

Helper functions

SLO-2 Vector operations 3D plots

S-9 SLO-1 Vector functions Axis modes Min-finding Heart sound classification using MATLAB Simulink

SLO-2 Vector indexing Multi plots in one figure

S-10 SLO-1 Matrix indexing Visualising matrices Numerical differentiation Access and explore the data Simulink library

SLO-2 Matrix indexing Visualising matrices

S-11

SLO-1 Indexing Colour maps Pre process and extract the features Connection

SLO-2 Indexing Colour maps Numerical integration

S-12 SLO-1

Advanced indexing Surface plots Differential equation Using decision tree classifier train the data Block specifications

SLO-2 Advanced indexing

Surface plots ODE solvers

S-13

SLO-1 Plotting Surf ODE solvers MATLAB Iteratively train Toll boxes

SLO-2 Plotting basics Surf options

S-14 SLO-1 Plot a straight line Contour ODE solvers syntax Evaluate Symbolic tool box

SLO-2 Plot a straight line Find

S-15 SLO-1 Plot knowledge trajectory Vectorization ODE functions Evaluate Symbolic variables

SLO-2 Plot knowledge trajectory Preallocation ODE functions: viewing results Symbolic expressions

Learning Resources

1. 2.

Learning Assessment

Bloom’s

Level of Thinking


CLA – 1 (10%) CLA – 2 (15%) CLA – 3 (15%) CLA – 4 (10%)#


Level 1 Remember

- 40% - 30% - 30% - 30% - 30% Understand

Level 2 Apply

- 40% - 40% - 40% - 40% - 40% Analyze

Level 3 Evaluate

- 20% - 30% - 30% - 30% - 30% Create

Total 100 % 100 % 100 % 100 % 100 %


Course Designers


Course Code

18AIS102J Course Name

SMART MANUFACTURING Course Category

S Engineering Sciences L T P C

1 0 3 4




Nil

Course Offering Department Data Book / Codes/Standards Nil



CLR-1 : Gain knowledge about Smart manufacturing 1 2 3 1 2 3 4 5 6 7 8 9 10

11

12

13

14

15

CLR-2 : Leaning about various types of sensors

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CLR-3 : Familiarizing Arduino controller and its interfacing

CLR-4 : Obtaining knowledge on Machine to Machine communication

CLR-5 : Creating insights to Virtual and Augmented Reality

CLR-6 : Knowing the security attacks and their counter measures



CLO-1 : Understand the impact of smart manufacturing 1 85

80

H

CLO-2 : Design real time applications with sensors 3 80

75

H H H H

CLO-3 : Interface devices with Aurdino controller 2 80

75

H H H H H

CLO-4 : Implement data transfer between devices 2 80

75

H H H

CLO-5 : Build AR and VR systems 3 75

70

H H H H H H

CLO-6 : Secure the information systems and networks 2 85

80

H H H

Duration (hour) 18 18 18 18 18

S-1 SLO-1 INTRODUCTION TO SMART MANUFACTURING : What is smart manufacturing ?

INTERCONNECTIVITY: Introduction to Arduino controller

MACHINE TO MACHINE (M2M)COMMUNICATION : Introduction to mobile networks

AUGMENTED REALITY (AR) AND VIRTUAL REALITY (VR) Introduction to AR and VR

CYBER SECURITY SYSTEMS: What is Cyber crime and security?

SLO-2 Drivers, enablers, forces and challenges of smart manufacturing

Basic structure Fixed networks and sensor networks VRML: Building objects Cyber attacks

S-2 SLO-1 Components of smart manufacturing

Input and output processing in Arduino controller

Access technologies VRML: Building world Types of attacks

SLO-2 Sensors: Introduction and types Timers in arduino M2M terminals and modules VRML: Adding light, sound effects Intruder detection systems

S-3 SLO-1 Flow and temperature sensors Programming Arduino Hardware and power interfaces VRML: Forming complex shapes Threats to information systems

SLO-2 Force, pressure and torque sensors

Simple code to be executed on Arduino

USB Interface VRML: Animations Threats to communication networks

S-3-6 SLO-1 Optical sensors Study of Arduino microcontroller interfacing

GPIO VRML: Adding colors and textures Study of different wireless network components

SLO-2 Design of automatic street lighting system using light sensors

Arduino microcontroller interfacing Designing LED wireless lamp Transformation of color model using VRML

Study of security system in mobile application

S-7- SLO-1 Humidity and water sensors Study of Basic sensors interfacing Oscilloscope Scene creation Firewalls: Introduction

10

SLO-2

Rain Alarm project Basic sensors interfacing Amplitude and frequency modulation Creation of 3D scene Configuration of firewalls

S 11-15

SLO-1

Gas sensor Brief description on GPS and Data logging

Study on IR rays Simulation of real time environment

Security in web browsers

SLO-2

Gas leakage detection system GPS and Data logging Designing an IR transmitter and receiver

Simulation of classroom Implementing security measures in web browser

Learning Resources

1. 1. J. Vetelino and A . Reghu, Introduction to sensors, CRC Press, 2010, ISBN 9781439808528. 2. J. Fraden, Handbook of Modern Sensors: Physics, Designs and Applications, 4th edition, Springer, 2010. 3.. J. Nussey, Arduino for Dummies, 1st edition, Wiley, 2013. ISBN: 9781118446379.

4. J. Edward Carryer, et al., Introduction to Mechatronic Design, Prentice Hall, 1st edition, 2010, ISBN: 978-8131788257. 5. Michael E Whitman and Herbert J Mattord, ―Principles of Information Security, Vikas Publishing House, New Delhi, 2003

Learning Assessment



CLA – 1 (10%) CLA – 2 (15%) CLA – 3 (15%) CLA – 4 (10%)#


Level 1 Remember 20% 20% 15% 15% 15% 15% 15% 15% 15% 15%

Understand

Level 2 Apply 20% 20% 20% 20% 20% 20% 20% 20% 20% 20%

Analyze

Level 3 Evaluate 10% 10% 15% 15% 15% 15% 15% 15% 15% 15%

Create

Total 100 % 100 % 100 % 100 % 100 %


Course Designers


Note: Syllabus for Other Subjects - Please refer Syllabus for All Core Subjects document

Documents

B.Tech in ARTIFICIAL INTELLIGENE Curriculum and Syllabus · B.Tech. in Artificial Intelligence Mission of the Department Mission Stmt - 1 To impart knowledge in cutting edge Computer