B.Tech Computer Science and Engineering

 

CS 361                                  Machine Learning                             3-0-0-6

 

 

Pre-requisites: MA 225 and MA 321

 

Syllabus: Mathematical foundations. Supervised Learning: information based learning (Decision trees), similarity based learning (NN, K-NN), probability based learning (Bayes methods), error based learning (Neural Networks), discriminant function based learning (SVM), evaluation measures. Theory of generalization. Bias-variance trade-off. Clustering (hierarchical clustering, partitional clustering,density based methods, graph based methods, non-negative matrix factorization, probabilistic methods, evaluation measures), dimensionality reduction (linear, non-linear): evaluation measures. Regression Methods: SVR, logistic regression. Density estimation methods: parametric, non-parametric, ensemble methods. Reinforcement learning.

 

Textbook:

 

  1. O. Duda, P. E. Hart, D. Strok, Pattern classification, 2nd edition, Wiley, 2000.
  2. Alpaydin, Introduction to Machine Learning, MIT Press, 2014.

References:

  1. M. Bishop, Pattern Recognition and Machine Learning, Springer, 2011.
  2. D. Kelleher, B. Mac Namee, A. D`arcy. Fundamentals of Machine Learning for Predictive Data Analytics - Algorithms, Worked Examples, and Case Studies, MIT Press, 2015.
  3. Shalev-Shwartz, S. Ben-David, Understanding Machine Learning From Theory to Algorithms, Cambridge University Press, 2014.
  4. Mitchell, Machine Learning, McGr