Machine Learning and Probabilistic Graphical Models
From the website:
Instructor: Sargur Srihari Department of Computer Science and Engineering, University at Buffalo
Machine learning is an exciting topic about designing machines that can learn from examples. The course covers the necessary theory, principles and algorithms for machine learning. The methods are based on statistics and probability– which have now become essential to designing systems exhibiting artificial intelligence. The course emphasisizes Bayesian techniques and probabilistic graphical models (PGMs). The material is complementary to a course on Data Mining where statistical concepts are used to analyze data for human, rather than machine, use.
The textbooks for different parts of the course are “Pattern Recognition and Machine Learning” by Chris Bishop (Springer 2006) and “Probabilistic Graphical Models” by Daphne Koller and Nir Friedman (MIT Press 2009).
Lecture slides and some videos of lectures.