Course Name: 

Machine Learning (IT307)


B.Tech (AI)




Programme Core (PC)

Credits (L-T-P): 

(3-0-2) 4


Introduction: Basic principles, Applications, Challenges. Supervised learning: Linear Regression with one variable and multiple variables, Gradient Descent, Classification, Logistic Regression, Overfitting, Regularization, Support Vector Machines, Artificial Neural Networks, Perceptrons, Multilayer networks, back-propagation, Decision Trees, Ensemble methods, Unsupervised learning: Clustering (K-means, K-mediods, Hierarchical), Dimensionality reduction: Principal Component Analysis, Applications of machine learning methods.


Ethem Alpaydin, ―Introduction to Machine Learning, Third Edition, MIT Press, 2014
Jason Bell,Machine learning Hands on for Developers and Technical Professionals‖, First Edition, Wiley, 2014
Peter Flach, ―Machine Learning: The Art and Science of Algorithms that Make Sense of Data, First Edition, Cambridge University Press, 2012.
Stephen Marsland, ―Machine Learning – An Algorithmic Perspective, Second Edition, Chapman and Hall/CRC Machine Learning and Pattern Recognition Series, 2014.
Tom M Mitchell, ―Machine Learning‖, First Edition, McGraw Hill Education, 2013.


Information Technology

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Head of the Department,
Department of Information Technology,
National Institute of Technology Karnataka,
SurathkalP. O. Srinivasnagar, Mangalore - 575 025
Ph.:    +91-824-2474056
Email:  hodit [at] nitk [dot] edu [dot] in

Web Admin: Sowmya Kamath S

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