Progress Report – Q1 2009

This is another progress report of my AI journey.  The top posts for the past quarter as follows:

• Wumpus World
• Stanford CS229 Class
• About
• What is Machine Learning?
• AIMA Part IV Acting Logically
• Progress Report – December 2008
• Logic, Programming and Prolog

Jan-Feb Wrap Up

Basically, I have been focusing on Probability and Statistics since I need a better grasp on this area of mathematics and its application to Machine Learning, in particular Bayesian Inference.  I am familiar with Bayes Theorem, but I am not familiar with Bayesian Belief and Markov Belief Networks, a topic covered in Judea Pearl’s book Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference.

I reviewing some other video lectures in Stanford University’s YouTube such as CS106A Program Methodology taught by Professor Mehran Sahami.  In addition I have reviewing a couple of MIT OpenCourse courses such as Introduction to Probability and Statistics, and Mathematics for Computer Science.

Self Pace Education

I have added three new links to my website – Stanford University on You Tube, MIT Open Courseware, and Video Lectures.  I found a number of self pace courses at Stanford University and MIT Open Courseware.  At Video Lectures, I found plenty of tutorials and conference workshops from many conferences from the last two years.  Enjoy exploring and expanding your horizons.

Stanford CS229 Class

Finally I finished the Machine Learning Video Lecture Series for CS229 taught by Dr. Andrew Ng, Assist Professor at Stanford University, on YouTube.  The course was excellent – it is broken into four sections :

• Supervised Learning,
• Learning Theory,
• Unsupervised Learning, and
• Reinforcement Learning.

The course materials on the website are excellent supplemental materials.  The problem sets does require the use of MATLAB (or the public domain version).  The course materials include sample practice midterm test.  The only weak spot is the Lecture Notes for Reinforcement Learning are brief and did not cover all material in the Lectures.   In addition, the references for additional provided by Dr. Ng are good since you can research additional information on a specific topic.  The material in this course is fairly current.  I recommend this course to anyone seriously interested in machine learning.