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Course Information
Identification

CS 335: Artificial Intelligence and Machine Learning Lab
 
Description

(1) Search: Uninformed search, A* search, adversarial search, local search.
(2) Planning: Markov Decision Problems, Value Iteration and Policy Iteration.
(3) Probabilistic reasoning: Bayes nets, conditional independence, exact and approximate inference.
(4) Supervised learning: Linear methods for classification and regression, regularisation, cross-validation, decision trees, neural networks, ensemble methods.
(5) Unsupervised learning: k-means clustering.
(6) Selected topics from natural language processing, robotics, computer vision, multi-agent systems.
 
References

(1) Artificial Intelligence: A Modern Approach, Stuart Russell and Peter Norvig, 3rd edition, Pearson, 2010.
(2) The Elements of Statistical Learning, Trevor Hastie, Robert Tibshirani, and Jerome Friedman, 2nd edition, Springer, 2009.
 
Home Page

Not Available
 
Prerequisites

N/A
 
Other Details

Duration : Full Semester Total Credit : 3
Type : Theory
 
Autumn Semester 2019-20

Status : Offered Instructor : Prof. Ganesh Ramakrishnan
 
Spring Semester 2019-20

Status : Not Offered Instructor : ---




Last Modified Date: 15-Jul-2013

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