#416 overall
Machine Learning (Tom Mitchell)
Carnegie Mellon University
1 recommender · 1 thread · recommended in 1 year, 2011 · score 1.0
Carnegie Mellon's graduate-level introductory machine learning course taught by Tom Mitchell, with lecture materials posted online. It covers the algorithms and the theory behind why they work.
Commenters say it gives a much better understanding of the algorithms than Andrew Ng's course, which they describe as good for implementation but light on insight.
Machine Learning & AIintermediatefreevideo lectures#machine learning#theory#algorithms#mitchell#carnegie mellon
Recommendations by year
20062026
I didn't like Ng's course on ML too much, it is very good if you just want to implement the algorithms, but it gives no insight on why they work.
I enjoyed far more Tom Mitchell's lectures, they gave me a much better understanding of the algorithms described:
http://www.cs.cmu.edu/~tom/10701_sp11/lectures.shtml
There are some comments on HN about it
http://news.ycombinator.com/item?id=3199718
What exactly do you want? Do you want a resource that "clicks" with you that you can use to learn about machine learning, or do you want a witty piece of art like "_why's poignant guide to ruby"?
If its the former you are looking for, I have several recommendations. There are a plethora of online resources to learn about machine learning from. In video form, my favorite resource is Yaser Abu-Mostafa's (Caltech prof) video lectures, available here: http://work.caltech.edu/teaching.html
You can also actually enroll in the next version of CS 156 (Abu-Mostafa's class) and do the class online with problem sets very similar to that of Caltech students actually enrolled in the class. (It starts January 8th).
Coursera/Udacity contain several classes that involve machine learning; Andrew Ng's class is a good place to start.
If you (like me) prefer text to video, you can buy Abu-Mostafa's book (I hear it follows the course very closely, and the book costs way less than $500 :D ), read Andrew Ng's online lecture notes, or read one of several freely available ML books (such as "Elements of Statistical Learning" or "Bayesian Reasoning and Machine Learning", but I've found these to take more mathematical maturity that the other resources recommended).
Note: One reason that I've highly recommended Abu-Mostafa's resources (besides having taken the class at Caltech and loving it) is that his class is more focused on the theory of machine learning (ie hammering in over-arching principles like avoiding overfitting by regularizing) rather than just covering as many algorithms as possible. Also, I believe Mitchell of CMU has good online resources for his machine learning course.