Machine Learning

Supervised Learning

- Lesson 0: Machine Learning is the ROX - Lesson 1: Decision Trees - Lesson 2: Regression and Classification - Lesson 3: Neural Networks - Lesson 4: Instance-Based Learning - Lesson 5: Ensemble B&B - Lesson 6: Kernel Methods and Support Vector Machines (SVM)s - Lesson 7: Computational Learning Theory - Lesson 8: VC Dimensions - Lesson 9: Bayesian Learning - Lesson 10: Bayesian Inference

Unsupervised Learning

- Lesson 1: Randomized optimization - Lesson 2: Clustering - Lesson 3: Feature Selection - Lesson 4: Feature Transformation - Lesson 5: Information Theory

Reinforcement Learning

- Lesson 1: Markov Decision Processes - Lesson 2: Reinforcement Learning - Lesson 3: Game Theory - Lesson 4: Game Theory, Continued

• Michael Littman - Michael Littman is a Professor of Computer Science at Brown University. He also teaches Udacity’s Algorithms course (CS215) on crunching social networks. Prior to joining Brown in 2012, he led the Rutgers Laboratory for Real-Life Reinforcement Learning (RL3) at Rutgers, where he served as the Computer Science Department Chair from 2009-2012. He is a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), served as program chair for AAAI's 2013 conference and the International Conference on Machine Learning in 2009, and received university-level teaching awards at both Duke and Rutgers. Charles Isbell taught him about racquetball, weight-lifting and Ultimate Frisbee, but he's not that great at any of them. He's pretty good at singing and juggling, though.
• Charles Isbell - Charles Isbell is a Professor and Senior Associate Dean at the School of Interactive Computing at Georgia Tech. His research passion is artificial intelligence, particularly on building autonomous agents that must live and interact with large numbers of other intelligent agents, some of whom may be human. Lately, he has turned his energies toward adaptive modeling, especially activity discovery (as distinct from activity recognition), scalable coordination, and development environments that support the rapid prototyping of adaptive agents. He is developing adaptive programming languages, and trying to understand what it means to bring machine learning tools to non-expert authors, designers and developers. He sometimes interacts with the physical world through racquetball, weight-lifting and Ultimate Frisbee.