Machine Learning
Course Name:
Machine Learning (CS312/CS312M)
Programme:
B.Tech (CSE)
Semester:
Sixth
Category:
Programme Specific Electives (PSE)
Credits (L-T-P):
04(3-0-2)
Content:
Introduction to machine learning, Supervised learning, Generative and discriminative learning, Regression,
Parametric and non-parametric learning, Classification, Principal component analysis, Model selection and
generalization, cross validation and resampling methods, Measuring classifier performance, Confusion matrix,
Decision tree, Neural Networks, Support Vector Machine, Naive Bayes,Voting Bagging boosting, Hidden Markov
Model, Unsupervised learning-Clustering methods, dimensionality reduction, kernel methods.
References:
Bishop, Christopher. Neural Networks for Pattern Recognition. New York, NY: Oxford University Press, 1995. ISBN:
9780198538646.
Duda, Richard, Peter Hart, and David Stork. Pattern Classification. 2nd ed. New York, NY: Wiley-Interscience,
2000. ISBN: 9780471056690.
Hastie, T., R. Tibshirani, and J. H. Friedman. The Elements of Statistical Learning: Data Mining, Inference and
Prediction. New York, NY: Springer, 2001. ISBN: 9780387952840.
MacKay, David. Information Theory, Inference, and Learning Algorithms. Cambridge, UK: Cambridge University
Press, 2003. ISBN: 9780521642989.
Mitchell, Tom. Machine Learning. New York, NY: McGraw-Hill, 1997. ISBN: 9780070428072.
Department:
Computer Science and Engineering