Principles of Artificial Intelligence and Machine Learning

Principles of Artificial Intelligence and Machine Learning course focuses on the development of algorithms and statistical models.

Course Objectives

  • To become familiar with basic principles of AI toward problem solving using Search Strategy.
  • To illustrate AI and ML algorithms and their use in appropriate applications.
  • To able to formulate solutions to real time problems using machine learning algorithms.
  • To design and analyze various machine learning algorithms and techniques with a modern outlook focusing on advances.

Course Syllabus

UNIT 1

Introduction: Definitions of AI, Foundations of AI, Subareas of AI

Intelligent Agents: Agents and Environment, Structure of Agents

Solving Problems by Searching:

Uninformed Search Strategies – Depth-first search – Breadth-first search – Uniform-cost search

Informed (Heuristic) Search Strategies – Heuristic Functions – Greedy best-first search – A* search

Hill climbing search

UNIT 2

Adversarial Search: Optimal Decisions in Games – MiniMax Algorithm, Alpha-Beta Pruning

Constraint Satisfaction Problems

Bayesian Learning: Bayes Theorem and Concept Learning – Maximum Likelihood and Least square Error Hypothesis – Bayes Optimal Classifier – Naïve Bayes Classifier

Minimum Description Length Principle – Gibbs Algorithm – Bayesian Belief Networks – EM Algorithm

UNIT 3

Introduction to Machine Learning: Examples of Machine Learning Applications – Learning Associations -Classification – Regression – Unsupervised Learning – Reinforcement Learning

Supervised Learning: Learning a Class from Examples – Vapnik-Chervonenkis Dimension – Probably Approximately Correct Learning – Noise – Learning Multiple Classes – Regression – Model Selection and Generalization – Dimensions of a Supervised Machine Learning Algorithm

UNIT 4

Artificial Neural Networks: Introduction, Appropriate problems for NN learning, Perceptrons, Multilayer Networks and Backpropagation Algorithm, Remarks on Backpropagation Algorithm, An example: Face recognition, Advanced topics of ANN-Error functions, Recurrent Networks

UNIT 5

Decision Trees: Introduction – Univariate Trees – Classification Trees – Regression Trees – Pruning – Rule Extraction from Trees – Learning Rules from Data – Multivariate Trees – Problems on Decision Tree

Reinforcement Learning: Introduction – The Learning Task – Q-Learning – Non deterministic rewards and actions – Temporal difference Learning – Generalizing from examples – Relationship to dynamic programming

Text Books

Reference Books

  • Elaine Rich, Kevin K and S B Nair, “Artificial Intelligence”, 3rd Edition, McGraw Hill Education, 2017.
  • Trevor Hastie, Robert Tibshirani& Jerome Friedman. “The Elements of Statistical Learning”, Springer Series in Statistics, 2nd Edition 2001.
  • Chrishtopher M.Bishop, “Pattern Recognition and Machine Learning”, ISBN-13: 978-0387-31073-2, Springer, 2006.

Online Resources

  • https://www.routledge.com/rsc/downloads/AI_FreeBook.pdf (EBook: Explorations in AI and Machine Learning by Prof. Roberto V. Zicari).
  • https://nptel.ac.in/courses/106105077 (Course Title: Introduction to Artificial Intelligence, Prof. AnupamBasu, Prof. S. Sarkar, IIT Kharagpur).
  • https://nptel.ac.in/courses/106105077 (Course Title: Introduction to Machine Learning, Prof. S. Sarkar, IIT Kharagpur).

Course Outcomes

After completion of the course students should be able to:

  • Understand the basics of various search techniques and learning algorithms.
  • Apply various search algorithms for problem solving.
  • Analyze Bayesian Networks, Game playing and constraint optimization methods.
  • Compare neural network parameter optimization using Gradient descent optimization and compute error function derivatives.
  • Analyze unsupervised, supervised and reinforcement learning.
  • Construct Neural Networks, Decision tree for problem solving.

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