Principles of Artificial Intelligence and Machine Learning
Exam Resources
I Mid-Term Solution Manual
Course Overview
Program: M.Tech (Wireless and Mobile Communications) — I Year, II Semester
Course Objectives
- Become familiar with basic principles of AI toward problem solving using Search Strategy
- Illustrate AI and ML algorithms and their use in appropriate applications
- Formulate solutions to real-time problems using machine learning algorithms
- Design and analyze various machine learning algorithms and techniques with a modern outlook focusing on advances
Course Units
| Unit | Topic | Hours |
|---|
| Unit 1 | AI Foundations & Search Strategies | ~10 hrs |
| Unit 2 | Adversarial Search & Bayesian Learning | ~10 hrs |
| Unit 3 | Introduction to ML & Supervised Learning | ~8 hrs |
| Unit 4 | Artificial Neural Networks | ~8 hrs |
| Unit 5 | Decision Trees & Reinforcement Learning | ~12 hrs |
Course Outcomes
After completing this course, students will be able to:
- Understand the basics of various search techniques and learning algorithms
- Apply classical and modern supervised/unsupervised/reinforcement learning algorithms to real-world problems
- Analyze probabilistic models, Bayesian learning, and ensemble techniques for effective decision-making
- Distinguish various neural networks, including modern deep learning models (CNNs)
- Analyze unsupervised, supervised and reinforcement learning
Text Books
- Stuart Russell, Peter Norvig — Artificial Intelligence: A Modern Approach, Pearson Education, 2nd Edition
- Tom M. Mitchell — Machine Learning, McGraw Hill Education, 1997
- Ethem Alpaydin — Introduction to Machine Learning, 3rd Edition, 2014
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, 2nd Edition, 2001
- Christopher M. Bishop — Pattern Recognition and Machine Learning, Springer, 2006
- Aurélien Géron — Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, O’Reilly, 3rd Ed 2022
Online Resources