DIPLOMA IN ARTIFICIAL INTELLIGENT
THEORY COURSE I
INTRODUCTION TO AI & PROGRAMMING TOOLS
- 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
- S. Russell and P. Norvig, “Artificial Intelligence: A Modern Approach‖, Prentice Hall, Third Edition, 2009.
- Allen B. Downey, “Think Python: How to Think Like a Computer Scientist‘‘, 2nd edition, Updated for Python 3, Shroff/O‘Reilly Publishers, 2016
- M. Tim Jones, ―Artificial Intelligence: A Systems Approach(Computer Science)‖, Jones and Bartlett Publishers, Inc.; First Edition, 2008
- Nils J. Nilsson, ―The Quest for Artificial Intelligence‖, Cambridge University Press, 2009.
- John V Guttag, ―Introduction to Computation and Programming Using Python‘‘, Revised and expanded Edition, MIT Press , 2013
- 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
- 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
- ‘Machine Learning’, Tom M. Mitchell, McGraw Hill.
- Neural networks, fuzzy logic and genetic algorithms, S. Rajasekaran, G.A. vijayalakshmi Pai, PHI Learning private Limited.
- Neuro-Fuzzy and Soft Computing, J.S.R. Jang, C.T. Sun, E. Mizutani, PHI Learning private Limited
THEORY COURSE III
NATURAL LANGUAGE PROCESSING
- 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
- Tanveer Siddiqui, U.S. Tiwary, “Natural Language Processing and Information Retrieval”, Oxford University Press, 2008.
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
- 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
- Nina Zumel, John Mount, “Practical Data Science with R”, Manning Publications, 2014.
- Jure Leskovec, Anand Rajaraman, Jeffrey D. Ullman, “Mining of Massive Datasets”, Cambridge University Press, 2014.
- Mark Gardener, “Beginning R – The Statistical Programming Language”, John Wiley & Sons, Inc., 2012.
- W. N. Venables, D. M. Smith and the R Core Team, “An Introduction to R”, 2013.
- Tony Ojeda, Sean Patrick Murphy, Benjamin Bengfort, Abhijit Dasgupta, “Practical Data Science Cookbook”, Packt Publishing Ltd., 2014.
- Nathan Yau, “Visualize This: The FlowingData Guide to Design, Visualization, and Statistics”, Wiley, 2011.
- Chris Eaton, Dirk deroos et al. , “Understanding Big data ”, McGraw Hill, 2012
PRACTICAL V: PYTHON & R PROGRAMMING LAB
List of Programs:
- Compute the GCD of two numbers.
- Find the square root of a number (Newton‘s method)
- Exponentiation (power of a number)
- Find the maximum of a list of numbers
- Linear search and Binary search
- Write a neural network program using back propagation learning in python.
- Find text ending with “ing” in a sentence using python.
- To study and implement basic functions and commands in R programming.
- To create a dashboard showing the sales and profits for different segments and sub-category of products across all the states.
- Create a Bar chart on a Map in Tableau.