Artificial Intelligence (CS 561)

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Introduction, Motivation of the course.
Problem Solving: Uninformed search, Informed search, local Search, Online search.
Knowledge and Reasoning: Propositional and Predicate Calculus, Semantic Nets, Frames, Scripts, Probabilistic Reasoning.
Learning: Introduction to machine learning paradigms: unsupervised, supervised, reinforcement learning, Naive Bayes, Decision Tree, Fundamental of Neural Networks and Deep Learning.
Evolutionary Computation: Genetic algorithms, Multi objective optimization, Differential Evolution, Particle Swarm and Ant Colony Optimization.
Application Topics: Introduction to NLP, Ethics and Bias in AI

Reference Books

  1. S. Russel and P. Norvig. Artificial Intelligence: A Modern Approach (Third Edition), Prentice Hall, 2009.
    Artificial Intelligence: A Modern Approach (3rd Edition) (
  2. E. Rich and K. Knight, Artificial Intelligence, Addison Wesley, 1990
    Artifical Inteliigence.pdf (
  3.  George Klir, U. St. Clair and B. Yuan, Fuzzy Set Theory: Foundations and Applications, Prentice Hall, 1997
  4. Ian Goodfellow, Yoshua Bengio and Aaron Courville, Deep Learnng, MIT Press, 2016
  5. Daphne Koller and Nir Friedman, Probabilistic Graphical Models: Principles and Techniques, MIT Press, 2009.


Lecture #Link to ClassTopic CoveredRemarks
1Class 1: 29 July 2023
2Class 2: 29 July 2023
3Class 3: 31 July 2023
4Class 4: 5 Aug 2023
5Class 5: 5 Aug 2023
6Class 6: 7 Aug 2023
7Class 7: 12 Aug 2023
8Class 8: 12 Aug 2023
9Class 9: 14 Aug 2023
10Class 10: 28 Aug 2023Local Search
Hill Climbing
11Weekend Class: 2 Sep 2023Hill Climbing
12Weekend Class: 2 Sep 2023 2nd Class
13Class 11: 4 Sep 2023
14Weekend Class 9 Sep 2023
15Weekend Class 10 Sep 2023
16Class 12: 11 Sep 2023
19Weekend Class: 16 Sep 2023Laplace Smoothing

Topic wise Learning Material:

CS50’s Introduction to Artificial Intelligence

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