PGD01C03
Core theory
4 credits
5 modules · 34 topics
Machine Learning for Data Science
Foundations of machine learning across supervised, unsupervised and reinforcement learning, ending with an introduction to neural networks.
Course outcomes
Have a strong foundation for machine learning.
Understand differences between supervised and unsupervised learning.
Learn reinforcement learning.
Modules
References
Christopher Bishop, Pattern Recognition and Machine Learning, Springer, 2007.
Kevin P. Murphy, Machine Learning: A Probabilistic Perspective, MIT Press, 2012.
Ethem Alpaydin, Introduction to Machine Learning, 3e, MIT Press, 2014.
Tom Mitchell, Machine Learning, McGraw-Hill, 1997.
Stanford Lectures of Prof. Andrew Ng.
NPTEL Lectures of Prof. B. Ravindran.