Course Details

Subject {L-T-P / C} : CS2010 : Introduction to AI and ML { 3-0-0 / 3}

Subject Nature : Theory

Coordinator : Dr. Tapas Kumar Mishra

Syllabus

Preamble, History, Philosophical Foundations, Architecture, Characteristics, Programming, Ethical Issues, Rule-based and Frame-based AI Expert System and applications with past, present, and future. General problem solving approaches, heuristic searching techniques, iterative search, uninformed search, adversarial search. Reasoning with uncertainty, Markov Models, Hidden Markov Models, Bayes’ rule, Bayes’ Nets: Representation, Independence and Inference. Machine Learning Paradigms, Supervised Learning, Naïve Bayes, Linear and Logistic Regression, Overfitting and underfitting, Decision Trees, Support Vector Machines, KNN, Neural Network, Ensemble learning. Unsupervised Learning, Introduction to Clustering, Partitional clustering, Hierarchical Clustering, Density-based clustering.

Course Objectives

Course Outcomes

1. Reason about the state-space search algorithm to use under different problem specific conditions. <br />2. Identify problems that are amenable to solution by AI methods. <br />3. Implement probabilistic solutions for decision making such as Hidden Markov Models, Bayes’ Networks, etc. <br />4. Learn and implement basic supervised methods like Decision Trees, Nearest Neighbours, Perceptron, Linear regression, Logistic regression, SVM and Ensemble Techniques. <br />5. Gain an understanding of basic unsupervised methods like Clustering.

Essential Reading

Supplementary Reading

Journal and Conferences