IT307
Course Name:
Machine Learning (IT307)
Programme:
B.Tech (AI)
Semester:
Fifth
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