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)

The students will learn some core AI/ML ideas

The courses concentrate on those topics that find applications in several areas rather than in the context of specific applications.

To know about specific AI/ML technique and its suitability for the given task.

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.

Stuart Russell, Peter Norvig, Artificial Intelligence – A Modern Approach, Pearson Education , 2009

Hal Daumé III, A Course in Machine Learning, zz , http://ciml.info/

Elaine Rich and Kevin Knight, Artificial Intelligence, 3rd Edition, McGraw-Hill , 2017

3. Trevor Hastie , Robert Tibshirani, The Elements of Statistical Learning,2nd Edition, Springer , 2009

AAAI, NIPS, ICLR, ICML

1. UC Berkeley CS188 Intro to AI Course Materials http://ai.berkeley.edu/home.html , 2. NPTEL course: An Introduction to Artificial Intelligence By Prof. Mausam | IIT Delhi https://onlinecourses.nptel.ac.in/noc21_cs42/preview , 3. NPTEL course: INTRODUCTION TO MACHINE LEARNING By Prof. B. Ravindran | IIT Madras https://archive.nptel.ac.in/courses/106/106/106106139/