Artificial Intelligence and Machine Learning

Course Objectives :                         Why this Subject?

1. To acquire knowledge on intelligent systems and agents
2. Learn the methods of solving problems using Artificial Intelligence
3. To understand fundamental concepts of machine learning and learning techniques
4. To understand various Machine learning algorithms related to classification and prediction

Course Outcomes (CO)

• CO1 Understand intelligent agents, search techniques and apply various problem-solving strategies to common AI applications
• CO2 Apply propositional logic and first order logic in reasoning
• CO3 Understand basic concepts of machine learning, its goals and applications.
• CO4 Analyse supervised learning techniques.

UNIT-I

Artificial Intelligence: Introduction to intelligent agents, Problem solving: Problem formulation, uninformed search strategies, heuristics, informed search strategies, constraint satisfaction Solving problems by searching, state space formulation, depth first and breadth first search, iterative deepening

UNIT-II

Logical Reasoning : Logical agents , propositional logic, inferences ,first-order logic, inferences in first order logic, forward chaining, backward chaining, unification, resolution

UNIT-III

Machine Learning: Introduction, Basic concepts: Definition of learning systems, Goals and applications of machine learning. Aspects of developing a learning system: training data, concept representation, function approximation. Types of Learning: Supervised learning and unsupervised learning. Overview of classification: setup, training, test, validation dataset, over fitting.

Classification Families: linear discriminative, non-linear discriminative, decision trees, probabilistic (conditional and generative), nearest neighbor.

UNIT-IV

Logistic regression, Perceptron, Exponential family, Generative learning algorithms, Gaussian discriminant analysis, Naive Bayes, Support vector machines: Optimal hyper plane, Kernels. Model selection and feature selection. Combining classifiers: Bagging, boosting (The Ada boost algorithm), Evaluating and debugging learning algorithms, Classification errors.

Textbook(s):

1. Rich and Knight, “Artificial Intelligence”, Tata McGraw Hill, 1992
2. Tom M Mitchell, Machine Learning, McGraw Hill Education

References:

1. S. Russel and P. Norvig, “Artificial Intelligence — A Modern Approach”, Second Edition, Pearson Edu
2. Introduction to Machine Learning - Ethem Alpaydin, MIT Press, Prentice hall of India.