Course Details

Subject {L-T-P / C} : CS2011 : Introduction to AI and ML { 2-0-0 / 2}

Subject Nature : Theory

Coordinator : Tapas Kumar Mishra

Syllabus

UNIT – I Preamble, History, Philosophical Foundations, Architecture, Characteristics, Programming, Ethical Issues, Rule-based and Frame-based AI Expert System and applications with past, present, and future. (6 Hours)
UNIT – II General problem solving approaches, heuristic searching techniques, iterative search, uninformed search, adversarial search. (12 Hours)
UNIT – III Reasoning with uncertainty, Markov Models, Hidden Markov Models, Bayes’ rule, Bayes’ Nets: Representation, Independence and Inference. (6 Hours)
UNIT – IV Machine Learning Paradigms, Supervised Learning, Naïve Bayes, Linear and Logistic Regression, Overfitting and underfitting, Decision Trees, Support Vector Machines, KNN, Neural Network, Ensemble learning. (12 Hours)
UNIT-V Unsupervised Learning, Introduction to Clustering, Partitional clustering, Hierarchical Clustering, Density-based clustering (4 Hours)

Course Objectives

Course Outcomes

After completing this course the student must demonstrate the knowledge and ability to:
CO1 Reason about the state-space search algorithm to use under different problem specific conditions.
CO2 Identify problems that are amenable to solution by AI methods.
CO3 Implement probabilistic solutions for decision making such as Hidden Markov Models, Bayes’ Networks, etc.
CO4 Learn and implement basic supervised methods like Decision Trees, Nearest Neighbours, Perceptron, Linear regression, Logistic regression, SVM and Ensemble Techniques.
CO5 Gain an understanding of basic unsupervised methods like Clustering.

Essential Reading

Supplementary Reading

Journal and Conferences