AI303

Course Name: 

Machine Learning (AI303)

Programme: 

B.Tech (AI)

Category: 

Programme Core (PC)

Credits (L-T-P): 

(3-0-2) 4

Content: 

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.

References: 

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.

Department: 

Information Technology
 

Contact us

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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