Lecture 17 | 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 continuous state MDPs, discretization, and policy and value iterations.

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
• continuous
• state
• MDP
• discretization
• fitted
• value
• iteration
• function
• approximate
• policy
• Markov
• decision
• process

[lovelplants]

oh nice.. @mayimark : ty

[mayimark]

@mayimark actually you can start at 19:40

[mayimark]

first 10 minutes just review the previous lecture. so you can skip first 10 min

[kapildalwani]

About Reinforcement learning