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.

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.

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

Christopher M. Bishop, and Nasser M. Nasrabadi, Pattern recognition and machine learning, Springer , 2006

AAAI, NIPS, ICLR, ICML,