CS7641 Machine Learning
This is a 3-course Machine Learning Series, taught as a dialogue between Professors Charles Isbell (Georgia Tech) and Michael Littman (Brown University).
Supervised Learning
Supervised Learning is a machine learning task that makes it possible for your phone to recognize your voice, your email to filter spam, and for computers to learn a number of fascinating things. This sort of machine learning task is an important component in all kinds of technologies. From stopping credit card fraud; to finding faces in camera images; to recognizing spoken language - our goal is to give students the skills they need to apply supervised learning to these technologies and interpret their output. This is especially important for solving a range of data science problems.
Unsupervised Learning
Ever wonder how Netflix can predict what movies you’ll like? Or how Amazon knows what you want to buy, before you make a purchase? The answer can be found in Unsupervised Learning. Closely related to pattern recognition, Unsupervised Learning is about analysing data and looking for patterns. It is an extremely powerful tool for identifying structure in data. This course focuses on how students can use Unsupervised Learning approaches - including randomized optimization, clustering, and feature selection and transformation - to find structure in unlabelled data.
Reinforcement Learning
Reinforcement Learning is the area of Machine Learning concerned with the actions that software agents ought to take in a particular environment in order to maximize rewards. You can apply Reinforcement Learning to robot control, chess, backgammon, checkers and other activities that a software agent can learn. Reinforcement Learning uses behavioural psychology in order to achieve reward maximization.
Lectures
- Week 1 - Decision Trees
- Week 1 - Chapter 1 Machine Learning
- Week 1 - Writing and reading papers
- Week 2 - Regression and classification
- Week 2 - Neural networks
- Week 3 - Instance Based Learning
- Week 3 - Ensemble Bagging and Boosting
- Week 4 - Support Vector Machines
- Week 5 - Computational Learning Theory
- Week 5 - Infinite hypothesis spaces
- Week 6 - Bayesian learning
- Week 6 - Bayesian Inference
- Week 7 - Information Theory
- Week 7 - Randomized Optimization
- Week 9 - Clustering
- Week 10 - Feature selection
- Week 10 - Feature transformation
- Week 11 - Markov Decision Processes
- Week 12 - Reinforcement learning
- Week 13 - Game theory
Links
Acknowledgements
Fixes to the notes for this course where provided by: