I happened to find these undergraduate ML course video clips opened on youtube by UBC. I think I really have to thank to UBC & the professor Nando de Freitas for this valuable sharing. Anyone with strong interest or something in ML should definitely be better to take a look through the whole sessions. That’s what I’m gonna do for months now.
Machine Learning and Probabilistic Graphical Models Course provided by Department of Computer Science and Engineering, University at Buffalo.
A bunch of educational video segments on machine learning – Machine Learning Video Library.
Topics included:
Aggregation, Bayesian Learning, Bias-Variance Tradeoff, Bin Model, Data Snooping, Error Measures, Gradient Descent, Learning Curves, Learning Diagram, Learning Paradigms, Linear Classification, Linear Regression, Logistic Regression, Netflix Competition, Neural Networks, Nonlinear Transformation, Occam’s Razor, Overfitting, Radial Basis Functions, Regularization, Sampling Bias, Support Vector Machines, Validation, VC Dimension
