Course Name: 

Foundations of Machine Learning (IT464) (2019 Curriculum)


B.Tech (IT)


Programme Specific Electives (PSE)

Credits (L-T-P): 

(3-0-2) 4


Linear algebra and probability theory basics – Machine learning- Types- Classification- Regression- Multi-class classification. dimensionality reduction –Linear and Logistic Regression. Naive Bayes, Parameter Estimation, Sequential Pattern Classification. Neural Network Basics – Backpropagation –Support Vector Machines, Kernel methods – Bias-Variance tradeoff. Regularization and model/feature selection. Ensemble Methods: Boosting, Bagging, Random Forests. Unsupervised learning – K-Means clustering- EM Algorithm – Reinforcement learning – introduction to deep learning. Recent Applications and trends of Machine Learning.


Understanding Machine Learning, Shai Shalev-Shwartz and Shai Ben-David. Cambridge University Press, 2017.
Tom M. Mitchell, -Machine Learning, McGraw-Hill Education (India) Private Limited, 2013.
Stephen Marsland, - Machine Learning: An Algorithmic Perspective, Second Edition, 2014.
Pattern recognition and machine learning by Christopher Bishop, Springer Verlag, 2006.


Information Technology

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