Edit (November 05, 2011): Note that this post was made over THREE years ago. That time this was the only comprehensive Machine Learning course available online. Since then situation has changed. Professor Andrew Ng’s course has been offered online for everyone. Many other courses have also become available. Find some of these at the bottom of this post.
Just two weeks ago I posted a few lectures on Machine Learning, Learning Theory, Kernel Methods etc on this post. Since then my friend and guide Sumedh Kulkarni informed me of a new course on the Stanford University youtube channel on Machine Learning I have also indexed this channel on my post on Video Lectures.
Since then I have already seen half of it, and though it covers a very broad range, and is meant to be a first course on Machine Learning, it is in my opinion the best course on the web on the same. Most others I find boring because of either poor English of the instructor or bad recording or both.
The course is taken by Dr Andrew Ng, who has very good experience in teaching this course and working in Robotics, AI and Machine Learning in general. Incidentally he has been the guide of a PhD candidate Ashutosh Saxena, whose research papers we have used for a previous project on pattern recognition.
Dr Ng’s deep knowledge in the field can be felt in just some minutes into the first course which he makes even more interesting by his good communication skills and ability to make lectures more exciting and intuitive by adding fun videos in between.
The course details are as follows.
Course: Machine Learning (CS 229). It can be accessed over here: Stanford Machine Learning.
Instructor: Dr Andrew Ng.
Lecture 1: Overview of the broad field of Machine Learning.
Lecture 2: Linear regression, Gradient descent, and normal equations and discussion on how they relate to machine learning.
Lecture 3: Locally weighted regression, Probabilistic interpretation and Logistic regression.
Lecture 4: Newton’s method, exponential families, and generalized linear models.
Lecture 5: Generative learning algorithms and Gaussian discriminative analysis.
Lecture 6: Applications of naive Bayes, neural networks, and support vector machine.
Lecture 7: Optimal margin classifiers, KKT conditions, and SUM duals.
Lecture 8: Support vector machines, including soft margin optimization and kernels.
Lecture 9: Learning theory, covering bias, variance, empirical risk minimization, union bound and Hoeffding’s inequalities.
Lecture 10: VC dimension and model selection.
Lecture 11: Bayesian statistics, regularization, digression-online learning, and the applications of machine learning algorithms.
Lecture 12: Unsupervised learning in the context of clustering, Jensen’s inequality, mixture of Gaussians, and expectation-maximization.
Lecture 13: Expectation-maximization in the context of the mixture of Gaussian and naive Bayes models, as well as factor analysis and digression.
Lecture 14: Factor analysis and expectation-maximization steps, and continues on to discuss principal component analysis (PCA).
Lecture 15: Principal component analysis (PCA) and independent component analysis (ICA) in relation to unsupervised machine learning.
Lecture 16: Reinforcement learning, focusing particularly on MDPs, value functions, and policy and value iteration.
Lecture 17: Reinforcement learning, focusing particularly on continuous state MDPs, discretization, and policy and value iterations.
Lecture 18: State action rewards, linear dynamical systems in the context of linear quadratic regulation, models, and the Riccati equation, and finite horizon MDPs.
Lecture 19: Debugging process, linear quadratic regulation, Kalmer filters, and linear quadratic Gaussian in the context of reinforcement learning.
Lecture 20: POMDPs, policy search, and Pegasus in the context of reinforcement learning.
Course Notes: CS 229 Machine Learning.
My gratitude to Stanford and Prof Andrew Ng for providing this wonderful course to the general public.
Other Machine Learning Video Courses:
1. Tom Mitchell’s Machine Learning Course.
1. Demystifying Support Vector Machines for Beginners. (Papers, Tutorials on Learning Theory, Machine Learing)
2. Video Lectures.