Machine Learning

http://cs229.stanford.edu/materials.html
https://www.youtube.com/watch?v=UzxYlbK2c7E

Four parts of the class:

• Supervise learning: providing the computer existing datasets that have the right answers.
• Regression (continuous data point) or classification (discrete data) are example of supervise learning
• Learning theory: understand how and why learning algorithm work (how to prove that algorithm that reads zip code works); what algorithm can approximate function, what size for the learning data we need.
• Unsupervised learning: being given a dataset and ask to find interesting structure (vs. giving the right answer)
• Clustering is one example
• Reinforcement learning: asked to make a sequence of decision over time;
• reinforce good behavior vs bad behavior: example flying helicopter: good behavior doesn't crash.