DIPLOMA IN ARTIFICIAL INTELLIGENT

DIPLOMA IN ARTIFICIAL INTELLIGENT

THEORY COURSE I

INTRODUCTION TO AI & PROGRAMMING TOOLS

OBJECTIVES:

  • To understand the Applications of AI & search strategies in AI.
  • To learn the different problem solving methods.
  • To learn to represent knowledge in solving AI problems.
  • To understand the different ways of learning the problems.
  • To know about the basics of python programming.

Unit I-Introduction                                                                                                   (8)

Introduction–Definition – Future & Applications of Artificial Intelligence –Intelligent Agents–Structure of Intelligent Agents – Problem solving– Search Strategies- Uninformed – Informed Search strategies.

Unit II-Problem Solving Methods                                                                                        (8)

Constraint Satisfaction Problems – Constraint Propagation – Backtracking Search – Game Playing – Optimal Decisions in Games – Alpha – Beta Pruning-Imperfect real time decisions-Games that include a Element of chance.

Unit III –First Order Logic                                                                                                   (8)

First Order Predicate Logic – Prolog Programming – Unification – Forward Chaining-Backward Chaining – Resolution – Knowledge Representation-Categories and objects.

Unit IV- Learning                                                                                                                 (8)

Learning from observations-Learning Decision Trees-Ensemble Learning-Knowledge in Learning –Explanation based Learning-Learning using relevance Information-Reinforcement Learning.

Unit V-Programming Tools                                                                                                  (8)

Python: – Basics Data Types, Conditional Statements, Looping, Control Statements,      String, List And Dictionary Manipulations, Python Functions,   Modules And   Packages, Object Oriented Programming in Python, Regular Expressions, Exception   Handling.

                                                                                                                             Total hrs: 40 Hrs 

TEXT BOOKS:

  1. S. Russell and P. Norvig, “Artificial Intelligence: A Modern Approach‖, Prentice Hall, Third Edition, 2009.
  2. Allen B. Downey, “Think Python: How to Think Like a Computer Scientist‘‘, 2nd edition, Updated for Python 3, Shroff/O‘Reilly Publishers, 2016  

REFERENCES:

  1. M. Tim Jones, ―Artificial Intelligence: A Systems Approach(Computer Science)‖, Jones and Bartlett Publishers, Inc.; First Edition, 2008
  2. Nils J. Nilsson, ―The Quest for Artificial Intelligence‖, Cambridge University Press, 2009.
  3. John V Guttag, ―Introduction to Computation and Programming Using Python‘‘, Revised and expanded Edition, MIT Press , 2013
  4. Robert Sedgewick, Kevin Wayne, Robert Dondero, ―Introduction to Programming in Python: An Inter-disciplinary Approach, Pearson India Education Services Pvt. Ltd., 2016.


THEORY COURSE II

MACHINE LEARNING AND DEEP LEARNING

OBJECTIVES:

  • To understand the Machine learning concept.
  • To learn about Deep Learning.
  • To know about Neural Network workings
  • To understand the Different Learning.

Unit I-Machine Learning                                                                                                            (8)

Introduction– What is Machine Learning? – Types of machine learning – Well Posed Learning Problems – Learning Problems – Designing a Learning System – A Concept Learning Task – Concept Learning as Search.

Unit II-Deep Learning with Neural Networks                                                                          (8)

Introduction – Fundamental of Deep Learning – Basic concepts of Neural Networks – Human Brain – Model of an Artificial Neuron– Neural Network Architecture – Characteristics – History of Neural Networks – Early Neural Networks Architecture – Learning Methods – Some Application Domains.

Unit III –Supervised Learning                                                                                                    (8)

Introduction – Perceptrons – Adaline – Back Propagation Multilayer Perceptrons – Modular Networks.    .

Unit IV- Learning from Reinforcement                                                                                     (8)

Introduction – Temporal Difference Learning – The Art of Dynamic Programming – Adaptive Heuristic Critic – Q-learning – A Cost Path Problem – Reinforcement Learning by Evolutionary Computation.

Unit V- Unsupervised Learning                                                                                                  (8)

Introduction – Competitive Learning Networks- Kohonen Self-Organizing Networks- Learning Vector Quantization- Hebbian Learning-Principal Component Networks- The Hop Field Networks

                                                                                                                             Total hrs: 40 Hrs 

TEXT BOOKS:

  1. ‘Machine Learning’, Tom M. Mitchell, McGraw Hill.
  2. Neural networks, fuzzy logic and genetic algorithms, S. Rajasekaran, G.A. vijayalakshmi Pai, PHI Learning private Limited.
  3. Neuro-Fuzzy and Soft Computing, J.S.R. Jang, C.T. Sun, E. Mizutani, PHI Learning private Limited

THEORY COURSE III

NATURAL LANGUAGE PROCESSING

OBJECTIVE:

  • Introduces the fundamental concepts and techniques of NLP.
  • Students will gain an in-depth understanding of the computational properties of natural languages.
  • To get familiar with extracting the meaning of an input sentence or an input textwhih is processed by a computer.

UNIT I: INTRODUCTION TO NATURAL LANGUAGE PROGRAMMING       (8)  

Overview:   What is Natural Language Processing (NLP) – Origins of NLP – Language and Knowledge – The Challenges of NLP- Language and Grammar-Processing Indian Languages- NLP Applications -Some Successful Early NLP Systems- Information Retrieval

UNIT II: LANGUAGE MODELING AND WORD LEVEL ANALYSIS                               (8)

Language Modeling – Introduction-Various Grammar-based Language Models-Statistical Language Model. Word Level Analysis: Overview- Regular Expressions.

Finite-State Automata – Morphological Parsing -Spelling Error Detection and Correction -Words and Word Classes- Part-of-Speech Tagging.

UNIT III- SYNTACTIC ANALYSIS AND SEMANTIC ANALYSIS                                    (8)

 Syntactic Analysis – Introduction – Context-Free Grammar-Contents –Constituency  Parsing-Probabilistic Parsing- Indian Languages. Semantic Analysis –Introduction- Meaning Representation- Lexical Semantics- Ambiguity – Word Sense –Disambiguation.

UNIT IV:  DISCOURSE PROCESSING AND NATURAL LANGUAGE                           (8)

Discourse Processing – Introduction – Cohesion – Reference Resolution- Discourse Coherence and Structure – Natural Language Generation – Introduction-Architectures of NLG Systems – Generation Tasks and Representations – Applications of NLG.

UNIT –V: MACHINE TRANSLATION                                                                                 (8)

Overview-Problems in Machine Translation- Characteristics of Indian Languages- Machine Translation Approaches- Direct Machine Translation-Rule-based Machine Translation- Corpus-based Machine Translation- Semantic or Knowledge-based MT systems- Translation involving Indian Languages

Total hrs: 40 Hrs      

TEXT BOOK:

  1. Tanveer Siddiqui, U.S. Tiwary, “Natural Language Processing and Information Retrieval”, Oxford University Press, 2008.

REFERENCE BOOKS:

1. Daniel Jurafsky and James H. Martin, ” Speech and Language Processing”, Pearson Education 2002

2. James Allen, “Natural Language Understanding”, Benjamin Cummings Publishing Co. 1995

THEORY COURSE IV

DATA SCIENCE AND DATA ANALYTICS

OBJECTIVES:

  • Able to apply fundamental algorithmic ideas to process data
  • Learn the mining and clustering
  • Be exposed to R programming
  • Learn tips and tricks for Big Data use cases and solutions.
  • Learn the different ways of Data Analysis
  • Be familiar with the visualization.

UNIT I INTRODUCTION TO DATA SCIENCE                                                          (8 hours)

Data science process – roles, stages in data science project – working with data from files – working with relational databases – exploring data – managing data – cleaning and sampling for modeling and validation – Introduction to NoSQL.

UNIT II  MODELING METHODS                                                                                (8 hours)

Choosing and evaluating models – mapping problems to machine learning, evaluating clustering models, validating models – cluster analysis – K-means algorithm, Naïve Bayes – Memorization Methods – Linear and logistic regression – unsupervised methods.

UNIT III  INTRODUCTION TO R                                                                                (8 hours)

Reading and getting data into R – ordered and unordered factors – arrays and matrices – lists and data frames – reading data from files – probability distributions – statistical models in R – manipulating objects – data distribution.

UNIT IV:  INTRODUCTION TO BIG DATA                                                              (8 hours)

 Introduction – distributed file system – Big Data and its importance, Four Vs, Drivers for Big data, Big data analytics, Big data applications. Algorithms using map reduce, Matrix-Vector Multiplication by Map Reduce.

UNIT V: DATA ANALYSIS AND VISUALIZATION USING TABLEAU              (8 hours)

Introduction to Tableau and its layout – Connecting tableau to files and databases – Data filters in Tableau – Calculation and parameters – Tableau graphs and maps – Creating Tableau dashboard-Data blending – Creating superimposed graphs – Integrating Tableau with R.

                                                                                                                              Total hrs: 40 Hrs

REFERENCES:

  1. Nina Zumel, John Mount, “Practical Data Science with R”, Manning Publications, 2014.
  2. Jure Leskovec, Anand Rajaraman, Jeffrey D. Ullman, “Mining of Massive Datasets”, Cambridge University Press, 2014.
  3. Mark Gardener, “Beginning R – The Statistical Programming Language”, John Wiley & Sons, Inc., 2012.
  4. W. N. Venables, D. M. Smith and the R Core Team, “An Introduction to R”, 2013.
  5. Tony Ojeda, Sean Patrick Murphy, Benjamin Bengfort, Abhijit Dasgupta, “Practical Data Science Cookbook”, Packt Publishing Ltd., 2014.
  6. Nathan Yau, “Visualize This: The FlowingData Guide to Design, Visualization, and Statistics”, Wiley, 2011.
  7. Chris Eaton, Dirk deroos et al. , “Understanding Big data ”, McGraw Hill, 2012
  8. http://www.johndcook.com/R_language_for_programmers.html
  9. http://bigdatauniversity.com/
  10. http://home.ubalt.edu/ntsbarsh/stat-data/topics.htm#rintroduction

PRACTICAL V: PYTHON & R PROGRAMMING LAB

List of Programs:

  1. Compute the GCD of two numbers.
  2. Find the square root of a number (Newton‘s method)
  3. Exponentiation (power of a number)
  4. Find the maximum of a list of numbers
  5. Linear search and Binary search
  6. Write a neural network program using back propagation learning in python.
  7. Find text ending with “ing” in a sentence using python.
  8. To study and implement basic functions and commands in R programming.
  9.  To create a dashboard showing the sales and profits for different segments and            sub-category of products across all the states.
  10. Create a Bar chart on a Map in Tableau.