Summary of Course Description · 2020. 3. 5. · Webdev 101 Tool 1 3 Getting data off the web with Python 2 6 Heavyweight Scraping with Scrapy 2 6 Introduction to NumPy 1 3 Introduction

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الجامعة وكالة

العلمي والبحث العليا للدراسات

Course Name: Statistics for Data Science Course Code: DS 611

Prerequisites: None Course Teaching Language: English

Course Level : 1 Credit Hours: ( 3 , 0 , 0 )

Course Description

This course covers the following topics: Fundamentals of probability theory and

statistical inference used in data science; Probabilistic models, random variables, useful

distributions, expectations, law of large numbers, central limit theorem; Statistical

inference; point and confidence interval estimation, hypothesis tests, linear regression.

The course will also focus on different types of quantitative research methods and

statistical techniques for analyzing data. Then, we will explore a range of statistical

techniques and methods using the open-source statistics language, R. The course will

also cover topics in quantitative techniques that include: descriptive and inferential

statistics, sampling, experimental design, parametric and non-parametric tests of

difference, ordinary least squares regression, and logistic regression. Recent correlated

software packages should be used through labs.

Course Objectives

The student should be able to:

1. Demonstrate understanding of the value of statistics and testing problems.

2. Demonstrate understanding of quantitative research methods and statistical

inferences for analyzing data.

3. Use statistical algorithms for solving binomial and exponential methods.

4. Apply statistical programming tools to solve real-world problems.

Summary of Course Description

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Learning outcomes: (comprehension, knowledge, intellectual & scientific skills) By completion of this course students are expected to be able to:

Demonstrate understanding of multiple testing problem,

probabilistic models, random variables, and useful distributions. Knowledge

Analyze data using different types of quantitative research

methods and statistical inferences. Cognitive Skills

Demonstrate knowledge of and ability to analyze statistical data

in a professional standards and professional behavior

Interpersonal Skills &

Responsibility

Communicate technical knowledge, including ideas, data analysis,

findings, or decision justification in written formats in a manner

appropriate to the audience.

Communication,

Information

Technology, Numerical

Course Content

List of topics Number of weeks

Teaching / contact hours

Fundamentals of probability theory and statistical inference used in data science

1 3

Probabilistic models 1 3 Random variables and useful distributions 1 3 Expectations and law of large numbers 1 3 Central limit theorem 1 3 Statistical inference; point and confidence interval estimation

2 6

Hypothesis tests, linear regression 2 6 Statistical techniques and methods using the open-source statistics language, R language

2 6

Descriptive and inferential statistics 1 3 Parametric and non-parametric tests of difference 1 3 Ordinary least squares regression 1 3 Logistic regression 1 3

3



Course Supportive Books & References

Book Title Author Publisher Publication

Year Practical Statistics for Data Scientists: 50 Essential Concepts ISBN-13: 978-1491952962

Peter Bruce

and Andrew Bruce O'Reilly Media 2017

R for Data Science: Import, Tidy, Transform, Visualize, and Model Data ISBN-13: 978-1491910399

Hadley Wickham and Garrett Grolemund O'Reilly Media 2017

https://www.amazon.com/Hadley-Wickham/e/B002BOA9GI/ref=sr_ntt_srch_lnk_3?qid=1550258165&sr=1-3

https://www.amazon.com/Hadley-Wickham/e/B002BOA9GI/ref=sr_ntt_srch_lnk_3?qid=1550258165&sr=1-3

https://www.amazon.com/Garrett-Grolemund/e/B079RKVV2Q/ref=sr_ntt_srch_lnk_3?qid=1550258165&sr=1-3

https://www.amazon.com/Garrett-Grolemund/e/B079RKVV2Q/ref=sr_ntt_srch_lnk_3?qid=1550258165&sr=1-3

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Course Name: Programming for Data

Science Course Code: DS 612


Course Level : 1 Credit Hours: 3

Course Description

This course presents the programming for data science, using the Python

programming language. The course covers the data science process, from

collecting data, pre-processing it (cleaning/correcting it), performing

exploratory data analyses, visualizing data, and sharing analysis results.

Course Objectives:

1. To learn the different aspects of programming for data science.

2. To gain an appreciation for the end-to-end process of obtaining

data, processing it, through to presenting results.

3. By the end of the course to be able to build own data simple data

processing pipeline.


Knowledge

Demonstrate an understanding of the process for

extracting knowledge from data.

Demonstrate an understanding of the broad range of

methods, which are based on statistics and computer


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and used in data science.

Cognitive Skills Apply the statistical and computational techniques in

data management, analysis and problem solving.

Communication,

Information


Evaluate the current tools and techniques used for

data science.

Course Content

List of topics Number of weeks Teaching / contact hours

Introduction to Data

Science 1 3

Python primer 2 6

Playing with Pandas 2 6

Data visualization 2 6

Big Data 2 6

Getting data from public

sources 2 6

Machine Learning 2 6

Sharing analyses 2 6


Book Title Author Publisher Publication Year Doing Data Science: Straight Talk from the Frontline

Cathy O'Neil, Rachel Schutt

O'Reilly Media 2013

Python 3 Object-Oriented Programming

Dusty Phillips Packt

Publishing 2018

6



Course Name: Data Visualization Course Code: DS 613



Course Description

Data Visualization enhances exploratory analysis as well as efficient communication of

data results. This course focuses on the design of visual representations of data in

order to discover patterns, answer questions, convey findings, drive decisions, and

provide persuasive evidence. The goal is to give you the practical knowledge you need

to create effective tools for both exploring and explaining your data. Exercises

throughout the course provide a hands-on experience using relevant programming

libraries and software tools to apply research and design concepts learned. Recent

correlated software packages should be used through labs.

Course Objectives


1. Demonstrate understanding of the value of visualized data.

2. Demonstrate understanding of analysis and efficient communication of data

results.

3. Use visual representations of data to discover patterns and answer questions.

4. Apply practical knowledge to create effective tools for both exploring and

explaining data.


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Course Content

List of topics Number of weeks Teaching / contact

hours

A language – learning bridge between Python and Java Script

1 3

Reading and writing data with Python 2 6 Webdev 101 Tool 1 3 Getting data off the web with Python 2 6 Heavyweight Scraping with Scrapy 2 6 Introduction to NumPy 1 3 Introduction to Pandas 1 3 Cleaning data with Pandas 1 3 Visualizing data with Matplotlib 2 6 Exploring data with Pandas 1 3

Delivering the Data 1 3

Demonstrate understanding of visualized data values

and design of visual representations. Knowledge

Analyze data to discover patterns and derive decisions

to support data – driven decision making. Cognitive Skills

Demonstrate knowledge to explore and explain data in

a professional standards and professional behavior.


Responsibility

Communicate technical knowledge, including ideas,

data analysis, findings, or decision justification in

written formats in a manner appropriate to the

audience.

Communication,

Information Technology,

Numerical

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Year

Data Visualization with Python

and JavaScript: Scrape, Clean,

Explore & Transform Your Data

1st Edition

ISBN-13: 978-1491920510

Kyran Dale O'Reilly Media 2016

Data Visualization: A Practical

Introduction 1st Edition

ISBN-13: 978-0691181622

Kieran Healy

Princeton

University

Press

2018

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Course Name: Applied Machine Learning Course Code: DS 614

Prerequisites: DS 612 Course Teaching Language: English


Course Description

Machine learning is a type of artificial intelligence (AI) that provides computers with

the ability to learn without being explicitly programmed. This area is also concerned

with issues both theoretical and practical. This course provides a broad and rigorous

introduction to machine learning. In this course, we will present algorithms and

approaches in such a way that grounds them in larger systems as you learn about a

variety of topics, including:

supervised learning

unsupervised learning

Reinforcement learning

This course covers bias-variance trade-off; training versus test error; overfitting; cross-validation; subset selection methods; regularized approaches for linear regression, non-parametric regression: trees, bagging, random forests; generalized additive models; support vector machines; k-means and hierarchical clustering; reinforcement learning, and principal components analysis for dimensionality reduction.


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Course Objectives


1. Provide a broad survey of approaches and techniques in machine learning

2. Develop a deeper understanding of several major topics in machine learning

3. Develop the design and programming skills that will help you to build intelligent,

adaptive artifacts

4. Develop the basic skills necessary to pursue research in machine learning.

5. Apply machine learning tools to solve real-world problems.


Knowledge Demonstrate understanding of classical machine

learning algorithm.

Cognitive Skills Apply the statistical and computational techniques in

data science to capture key patterns.


Responsibility

Show an ability to practice in a professional standards

and professional behavior.

Demonstrate ability to decide what techniques are

appropriate for a given question, and to make trade-offs

between model complexity in a professional standards

and professional behavior.

Communication,


Numerical




audience.

Evaluate the current tools and techniques used for data

science.

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Course Content

List of topics Number of

weeks

Teaching /

contact hours

Pattern Recognition Introduction Pattern recognition definition Types of Learning Supervised learning unsupervised learning Complete ML Example

1 3

Python for Machine Learning : Scikit-learn for working with classical ML

algorithms Pandas for data extraction and preparation Matplotlib for data visualization

1 3

K-Nearest Neighbour Classifier Describe a data set as points in a high dimensional

space. Compute distances between points in high

dimensional space. Implement a K-nearest neighbor model of

learning. Draw decision boundaries. First “machine learning” algorithm K-nearest neighbor for classification & Regression Handwriting recognition sample

1 3

Neural Network Decision Stump Single-layer neural networks How Neuron work Perceptron Learning Rule Gradient decent/Backpropagation Multi-layer neural networks.

2 6

Decision Trees What is a Decision Tree Sample Decision Trees How to Construct a Decision Tree

1 3

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ID3 algorithm Problems with Decision Trees Naïve Bayes Classifier Probability Basics Probabilistic Classification Naïve Bayes Principle and Algorithms Example: Play Tennis

1 3

Margin-Based Classification Support Vector Machines

1 3

Unsupervised learning - Clustering & K-Mean Unsupervised learning Clustering & Types of clustering K-Means clustering Simple K-Means example Weaknesses of K-Mean Clustering Applications of K-Mean Hierarchical clustering Agglomerative Clustering Common Distance measures inter-Cluster Similarity

2 6

Ensemble, Bagging, Boosting, Stacking Ensemble, Bagging, Random Forest Boosting, AdaBoost Stacking

1 3

Features Engineering Feature selection Features generation Dimensionality reduction

1 3

Dataset management Dataset splitting Cross validation Validation & Verification Underfitting and Overfitting

1 3

https://github.com/harvard-ml-courses/cs181-lectures/blob/master/s17/10.handout.pdf

https://github.com/harvard-ml-courses/cs181-lectures/blob/master/s17/11.handout.pdf

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Time Series recognition Hidden Markov Model

1 3

Reinforcement Learning MDP / Value and Policy Iteration Reinforcement Learning

1 3



Year Pattern Recognition and Machine Learning (Information Science and Statistics),:

Christopher M. Bishop Springer

2016

Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)

Kevin P. Murphy Francis Bach 2012

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Course Name: Big Data Analytics Course Code: DS 615

Prerequisites: Statistics for Data

Sciences (DS 611) Course Teaching Language: English

Course Level : 2 Credit Hours: 3

Course Description

Big Data requires the storage, organization, and processing of data at a scale and

efficiency that go well beyond the capabilities of conventional information

technologies. In this course, we will study the state of the art in big data

management: we will learn about algorithms, techniques and tools needed to

support big data processing. In addition, we will examine real applications that

require massive data analysis and how they can be implemented on Big Data

platforms.

Course Objectives

1. Treat the Big Data storage, processing, analysis, visualization, and

application issues.

2. Get insight on what tools, algorithms, and platforms to use on

which types of real world use cases.


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Knowledge Demonstrate an understanding of the broad range of methods, which are based on statistics and computer and used in data science.

Cognitive Skills Apply the statistical and computational techniques in data management, analysis and problem solving.


Responsibility Show an ability to practice in a professional standards and professional behavior.

Communication,

Information


Evaluate the current tools and techniques used for data science.

Course Content

List of topics Number of weeks Teaching /

contact hours

Background - Course Overview; The evolution of Data Management and introduction to Big Data - Introduction to Databases, Relational Model and SQL

2 6

Big Data Foundations and Infrastructure (3 weeks) - Introduction to Map Reduce - Algorithm Design for MapReduce: Relational Operations - MapReduce Algorithm Design Patterns; Parallel Databases vs MapReduce

4 12

Transparency and Reproducibility - Data Exploration and Reproducibility

2 6

https://www.vistrails.org/index.php/Course:_Big_Data_2015#Week_5_-_March_9:_MapReduce_Algorithm_Design_Patterns.3B_Parallel_Databases_vs_MapReduce



https://www.vistrails.org/index.php/Course:_Big_Data_2015#Transparency_and_Reproducibility__.281_week.29

https://www.vistrails.org/index.php/Course:_Big_Data_2015#Week_6_-_March_23:_Data_Exploration_and_Reproducibility

https://www.vistrails.org/index.php/Course:_Big_Data_2015#Week_6_-_March_23:_Data_Exploration_and_Reproducibility

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Big Data Algorithms, Mining Techniques, and Visualization (6 weeks) - Finding similar items - Association Rules - Visualization and Spatio-Temporal Data - Parallel Databases - Graph Analysis

7 21


Book Title Author Publisher Publication Year Mining of Massive Datasets

Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman

Cambridge University Press

2014

Data-Intensive Text Processing with MapReduce

Jimmy Lin, Chris Dyer

Morgan and Claypool Publishers

2010

https://www.vistrails.org/index.php/Course:_Big_Data_2015#Big_Data_Algorithms.2C_Mining_Techniques.2C_and_Visualization_.286_weeks.29



17



Course Name: Statistical Methods for

Discrete Response and Time Series

Course Code: DS 621


Course Level : ----- Credit Hours: ( 3 , 0 , 1 )

Course Description

Classical linear regression and time series models are workhorses of modern statistics,

with applications in nearly all areas of data science. This course takes a more advanced

look at both classical linear and linear regression models, including techniques for

studying causality, and introduces the fundamental techniques of time series

modeling. Mathematical formulation of statistical models, assumptions underlying

these models, the consequence when one or more of these assumptions are violated,

and the potential remedies when assumptions are violated are emphasized

throughout. Major topics include classical linear regression modeling, casual inference,

identification strategies, and a class of time series models that are popular among

industry professionals. The course emphasizes formulating, choosing, applying, and

implementing statistical techniques to capture key patterns exhibited in data. All of the

techniques introduced in this course come with real-world examples and R code that is

explained in weekly sessions. Students who successfully complete this course will be

able to decide what techniques are appropriate for a given question, and to make

trade-offs between model complexity, ease of interpreting results, and timing

implementation in real-world applications. As concepts in probability theory and

mathematical statistics are used extensively; students should feel comfortable with the

definition, manipulation, and application of these concepts in mathematical notations.


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Recent correlated software packages should be used through labs.

Course Objectives


1. Demonstrate understanding of classical linear regression and time series models.

2. Demonstrate understanding of causality techniques and mathematical

formulation of statistical models.

3. Use statistical techniques for formulating, choosing, applying, and implementing

data.



Demonstrate understanding of classical linear regression and

time series models. Knowledge

Analyze large data to study causality and introduce fundamental

techniques of time series modeling.

Apply the statistical and computational techniques in data

science to capture key patterns.

Cognitive Skills

Demonstrate knowledge of and ability to decide what techniques

are appropriate for a given question, and to make trade-offs

between model complexity in a professional standards and

professional behavior.


Responsibility

Communicate technical knowledge, including ideas, data

analysis, findings, or decision justification in written formats in a

manner appropriate to the audience.

Communication,

Information


19



Course Content


hours

Basic Regression Models 1 3 Discrete hazard functions and parametric regression models

1 3

Discrete and continuous hazards 1 3 Characteristics of Time Series: Nature of time series, time series statistical models

1 3

Measures of Dependence 1 3 Stationary Time Series 1 3 Estimation of Correlation 1 3 Classical Regression in the Time series context

1 3

Exploratory data analysis 1 3 Smoothing in the time series context 1 3 Autoregressive moving average models 1 3 Difference equations 1 3 Forecasting 1 3 Estimation 1 3 Cyclical behavior and periodicity 1 3



Year Time Series Analysis and Its Applications: With R Examples ISBN-13: 978-3319524511

Robert H. Shumway David S. Stoffer

Springer

2017

Modeling Discrete Time-to-Event Data ASIN: B01H3AIL2I

Gerhard Tutz Matthias Schmid

Springer

2016

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Course Name: Data systems Course Code: DS 622

Prerequisites: Course Teaching Language: English Course Level : Credit Hours: 3

Course Description

This course will be a comprehensive introduction to modern data systems. The

primary focus of the course will be on modern trends that are shaping the data

management industry right now such as column-store and hybrid systems,

shared nothing architectures, cache conscious algorithms, hardware/software

co-design, main memory systems, adaptive indexing, stream processing,

scientific data management, and key-value stores. We will also study the history

of data systems, traditional and seminal concepts and ideas such as the relational

model, row-store database systems, optimization, indexing, concurrency control,

recovery and SQL; In this way, we will discuss both how data systems evolved

over the years and why, as well as how these concepts apply today and how data

systems might evolve in the future.

Course Objectives

1. Learn state-of-the-art research and industry trends in big data systems. 2. Understand the tradeoffs in designing and implementing modern big data

systems. 3. Be able to make design decisions in big data driven scenarios.

This course covers main NoSQL data management systems topics such as key-

value stores, graph databases, and document databases.


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Knowledge

Demonstrate an understanding used to create, manipulate

and optimize database as well as of splitting data across

machines via sharding.

Cognitive Skills

Apply methods to analyze large data sets using

aggregation techniques to support data-driven decision

making.

Develop software and tools to create large scale database

application that are effective for analysis.


Responsibility

Show an ability to work effectively as an individual and as

a member of a team to accomplish a goal.

Communication,

Information


Evaluate the current tools and techniques used for data systems.

Course Content

Topic Number of

Weeks Contact Hours

row-store database systems, optimization, indexing, concurrency control, recovery

2 6

Introduction to NOSQL database systems 1 3 NOSQL Creating , Inserting, Updating and deleting Documents (Chodorow Chapter 3)

2 6

NOSQL Querying (Chodorow Chapter 4) 2 6 NOSQL Indexing (Chodorow Chapter 5) 1 3 NOSQL Aggregation (Chodorow Chapter 7) 1 3 Sharding ( Chodorow chapter 13,14,15 ) 2 6 Manipulating Graph database 2 6 Key- value database 2 6

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Year

Fundamentals of Data

base Systems

Elmasri &

Navathe Pearson 5102

NoSQL for Mere Mortals Dan Sullivan Pearson

Education 2015

MongoDB: The

Definitive Guide Chodorow, K.

Pearson

Education 5102

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Course Name: Deep Learning Course Code: DS 623

Prerequisites: DS 614 Course Teaching Language: English

Course Level : Postgraduate Credit Hours: ( 3 , 0 , 0 )

Course Description

This is an advanced course on machine learning, focusing on recent advances in deep

learning with neural networks, such as CNN, RNN, Deep RL networks. The course will

concentrate cover computer vision and natural language processing (NLP)

applications. Recent statistical techniques based on neural networks have achieved a

remarkable progress in these fields, leading to a great deal of commercial and

academic interest. The course will introduce the mathematical definitions of the

relevant machine learning models and derive their associated optimization

algorithms. It will cover a range of applications of neural networks in natural language

processing, including analyzing latent dimensions in text, translating between

languages, and answering questions.

Course Objectives


1. Understand the definition of a range of advanced machine learning models.

2. Understand advanced machine learning implementations mechanisms and

sequence embedding models and how these modular components can be


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combined to build state-of-the-art systems.

3. Have an understanding of how to choose a model to describe a particular type of

data.

4. Know how to evaluate a learned model in practice.

5. Understand the mathematics necessary for constructing novel machine learning

solutions.

6. Be able to design and implement various machine learning algorithms in a range

of real-world applications.


Knowledge

Demonstrate an understanding of the broad range of advanced machine learning algorithm, which are based on statistics and computer and used in data science.

Cognitive Skills

Apply the deep learning models and computational

techniques in data science to capture key patterns in

different real world application.


Responsibility

Demonstrate ability to decide what techniques are

appropriate for a given question, and to make trade-

offs between model complexity in a professional

standards and professional behavior.

Communication,


Numerical




audience.

Evaluate the current tools and techniques used for

data science.

25



Course Content


hours

Introduction to Deep learning - Deep Learning Models

1 3

Deep Learning development tool: Introduction to Theano, TensorFlow, and

Keras libraries. Introduction Machine Learning With

Weka Seaborn for data visualization library

2 6

Deep Learning for Computer vision Convolutional Neural Networks Improve Model Performance With Image

Augmentation. Object detection Object tracking and action recognition Famous computer vision deep learning

architecture such as AlexNet Reduce Overfitting With Dropout

Regularization Transfer Learning

3 9

Natural Language Processing Intro and text classification

2 3

Time Series Prediction Time series problem RNN Networks LSTM Networks, GRU Networks

2 6

NLP & Deep Learning Vector Space Models of Semantics Word-2-Vec Sequance-2-Sequance Sequence to sequence tasks Dialog systems

2 6

Deep Reinforcement Learning 1 3

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Generative adversarial network 1 3 State of the art XGBoost 1 3



Year

Neural Networks and Deep

Learning: A Textbook Charu C. Aggarwal 2018

Keras 2.x Projects: 9 projects

demonstrating faster

experimentation of neural network

and deep learning applications

using Keras

Giuseppe Ciaburro 2018

Advanced Deep Learning with

Keras: Apply deep learning

techniques, autoencoders, GANs,

variational autoencoders, deep

reinforcement learning, policy

gradients, and more

Rowel Atienza 2018

27



Course Name: Generalized Linear Models Course Code: DS624



Course Description

The course focus on analyzing linear and non-linear effects of continuous and

categorical predictor variables on a discrete or continuous dependent variable using

Generalized linear models. The structural form of the model describes the patterns of

interactions and associations. The model parameters provide measures of strength of

associations. In models, the focus is on estimating the model parameters. The basic

inference tools (e.g., point estimation, hypothesis testing, and confidence intervals)

will be applied to these parameters.

Course Objectives

Upon successful completion of the course students should:

1. Understand statistical concepts for building generalized linear models.

2. Be able to apply the concepts of mathematics and statistics in estimating the model

parameters.

3. Use multiple regression, analysis of variance and analysis of covariance for

quantitative responses.

4. Use logistic regression and probit models for binary data



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Demonstrate understanding of simple and multiple

regression as well as generalized linear models. Knowledge

Apply the most common generalized linear models in

statistical data analysis of the all areas of application. Cognitive Skills

Demonstrate knowledge of and ability to determine

which model is appropriate for a given application area.


Responsibility

have the ability to present and discuss, orally and in

writing, the results of studies based on generalized linear

models

Communication,


Numerical

Course Content


contact hours

Introduction Simple Regression Model

2 6

Bivariate Regression 1 3

On way ANOVA 1 3

ANOVA and the Bivariate Regression Approach

1 3

Multiple Regression Model 1 3

Multiple Regression Model when predictors interact

1 3

Two way ANOVA 1 3

Logistic regression models 1 3

Analysis of Covariance: Continuous and Categorical Predictors

1 3

29



Repeated Measures 1 3

Multiple Repeated Measures 1 3

Mixed Between and within Designs 1 3

Poison Regression 1 3

Log-Linear Models 1 3



Year An Introduction to Generalized Linear Models, ISBN: 9781351726221.

Annette J. Dobson, Adrian G. Barnett

CRC Press

April 2018

Regression, ANOVA, and the General Linear Model, ISBN: 9781483310336 Peter W. Vik

SAGE Publications,

Inc

2013

31



Course Name: Artificial Intelligence Course Code: DS 625



Course Description

Artificial Intelligence (AI) is an exciting field that has enabled a wide range of cutting-

edge technology, from driverless cars to grandmaster-beating Go programs. The goal

of this course is to introduce the ideas and techniques underlying the design of

intelligent computer systems. Topics covered in this course are broadly being divided

into 1) planning and search algorithms, 2) probabilistic reasoning and representations,

and 3) machine learning. Within each area, the course will also present practical AI

algorithms being used in the wild and, in some cases, explore the relationship to state-

of-the-art techniques in robotics, computer vision, and related areas. Recent

Correlated software packages should be used through labs.

Course Objectives


1. Demonstrate understanding of major artificial intelligence techniques.

2. Demonstrate understanding of planning and searching algorithms.

3. Use a set of probabilistic reasoning and representation algorithms.

4. Apply technical knowledge and techniques for problem solving and reliability

of results.


31




Demonstrate understanding of understanding of major artificial

intelligence techniques. Knowledge

Analyze large data sets for planning and searching algorithms. Cognitive Skills

Demonstrate knowledge to apply Artificial Intelligence

algorithms in a professional standard.

Interpersonal Skills

& Responsibility


analysis, findings, or decision justification in written formats in

a manner appropriate to the audience.

Communication,

Information

Technology,

Numerical

Course Content


hours Intelligent Agents 1 3

Solving problems by searching 2 6

Beyond classical search 2 6

Advanced search 1 3

Logical agents 1 3

Constraint satisfaction problems 1 3

First order logic 1 3

Inference in First order logic 1 3

Classical planning 1 3

Knowledge representation 1 3

32



Quantifying uncertainty 1 3

Natural language processing 2 6



Year

Artificial Intelligence: A modern

approach 3rd Edition

ISBN-13: 978-9332543515

Stuart Russell Pearson

Education India 2015

33



Course Name: Data Engineering Course Code: DS 626


Course Level : ----- Credit Hours: ( 3 , 0 , 0)

Course Description

The course will focus on the analysis of messy, real life data to perform predictions

using statistical and machine learning methods. Material covered will integrate the five

key facets of an investigation using data: (1) data collection - data wrangling, cleaning,

and sampling to get a suitable data set; (2) data management - accessing data quickly

and reliably; (3) exploratory data analysis – generating hypotheses and building

intuition; (4) prediction or statistical learning; and (5) communication – summarizing

results through visualization, stories, and interpretable summaries.

Course Objectives


1. Demonstrate understanding of storing, managing and processing of datasets.

2. Demonstrate understanding of analyzing large datasets for data storage,

retrieval, and processing systems.

3. Use a set of building blocks to construct a complete architecture for storing

and processing data.


34



4. Apply technical knowledge and architectures for problem solving and

reliability of results.


Demonstrate understanding of data collection - data

wrangling, cleaning, and sampling.

Knowledge

Analyze large data sets to support decision-making processes. Cognitive Skills

Demonstrate knowledge to manage data quickly and reliably

in a professional standard.

Interpersonal

Skills &

Responsibility


analysis, findings, or decision justification in written formats

in a manner appropriate to the audience.

Communication,

Information

Technology,

Numerical

Course Content


contact hours

Statistical Learning and Regression 1 3

Curse of Dimensionality and Parametric Models and Assessing Model Accuracy and Bias-Variance Trade-off

1 3

Classification Problems and K-Nearest Neighbors 1 3

Introduction to R 1 3

Linear Regression 1 3

Classification 2 6

Resampling Methods 1 3

35



Linear Model Selection and Regularization 2 6

Moving Beyond Linearity 1 3

Tree-Based Methods 1 3

Support Vector Machines 2 6

Unsupervised Learning 1 3



Year An Introduction to Statistical Learning: with Applications in R 1st Edition ISBN-13: 978-1461471370

Gareth James, Daniela Witten, Trevor Hastie,

Robert Tibshirani

Springer 2016