**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.

**Course Overview:**

*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: F*actor 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.

**Related Posts:**

1. Demystifying Support Vector Machines for Beginners. (Papers, Tutorials on Learning Theory, Machine Learing)

2. Video Lectures.

on December 15, 2008 at 2:08 am |jkwiens.com: Genetic Algorithms[...] Learning after reading several posts at Onionesque Reality. This lead me to start watching an Online Machine Learning course at Stanford. I haven’t listened to the entire set of lectures yet, but hopefully I will have [...]

on January 23, 2009 at 6:27 am |Demystifying Support Vector Machines for Beginners « Onionesque Reality[...] provided by Stanford university. It too is suited for an introduction to Support Vector Machines. Please find the post here. Also this comment might be helpful, suggestions to it according to your experience are also [...]

on February 4, 2011 at 2:06 pm |Another Word For It[...] The Best Machine Learning Course on the Web by Shubhendu Trivedi. [...]

on November 3, 2011 at 12:07 am |michaiel.mason@dmx.comThere are lots of good machine learning courses on the web. I did my Msc at Edinburgh, and Storkey’s Machine Learning and Pattern Recognition course there was very solid, well motivated, and covered some more detail than Andrew Ngs course. No online lecture videos though I am afraid (We had them internally but they don’t seem to be available outside Edinburgh). But the course notes are all there. Some things are missing: the focus is on probabilistic interpretations, so the kernel methods stuff focuses on Gaussian processes rather than SVMs for example. But altogether very solid.

on November 3, 2011 at 12:15 am |Shubhendu TrivediI can imagine. Edinburgh has a strong neural computation/ML programme. Some important ML people have done their PhDs from there.

This post though was made exactly three years ago. At that time, there were not many good Machine Learning courses available on the web. This was the first one that was solid enough.

I do agree that it focuses more in Gaussian Processes (but to be honest I thought more could have been done. That said that might have needed the lectures to be 40 rather than 20!). Also what I was disappointed by was the lack of focus on Neural Networks. But recently Dr. Ng came up with a lecture series on unsupervised feature learning that only talks about Neural Nets. That evens out I suppose.