Lecture 16 | Machine Learning (Stanford)

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng discusses the topic of reinforcement learning, focusing particularly on MDPs, value functions, and policy and value iteration.

This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed.

Complete Playlist for the Course:
http://www.youtube.com/view_play_list?p=A89DCFA6ADACE599

CS 229 Course Website:
http://www.stanford.edu/class/cs229/

Stanford University:
http://www.stanford.edu/

Stanford University Channel on YouTube:
http://www.youtube.com/stanford

Category:

Education

Tags:

• science
• math
• engineering
• computer
• technology
• robotics
• reinforcement
• learning
• algorithm
• MDP
• Markov
• decision
• process
• policy
• value
• function
• iteration
• bellman
• equation

[MatthewHudghton]

This has been very useful to me! thank you!

[GilCohen82]

This is a very good introduction lecture on Reinforcement Learning!

[jacobakkerboom]

In the computation you start at 57, to determine why moving west is better than moving north from the (3,1) state, it seems that you disregarded or forgot the discount factor, without mentioning it. I do think that in this case it suffices to look at undiscounted values to determine the optimal action, because there are no intermediate rewards. I find find this just a bit misleading, but I also wanted to share my thoughts. Great lecture (so far)!