Artificial Intelligence (CS 561)

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Syllabus

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) (ucla.edu)
  2. E. Rich and K. Knight, Artificial Intelligence, Addison Wesley, 1990
    Artifical Inteliigence.pdf (mit.edu)
  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.

Classes

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
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18
19Weekend Class: 16 Sep 2023Laplace Smoothing
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21
22

Topic wise Learning Material:

TopicLinkRemarks
Search
BFS
DFS
A*
CS50’s Introduction to Artificial Intelligence

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