Course Identification

This course will provide a self-contained introduction to some of the actively-researched areas in machine learning today. It will cover theoretical principles and challenges as well as practical algorithms. The focus will be on supervised and discriminative learning, where the goal is to learn good predictors from data while making few or no probabilistic assumptions. Along the way, we will introduce and use tools from probability, game theory, convex analysis and optimization. The course will cover the following topics (time permitting):

• Statistical learning: Statistical learning models; Overfitting; Generalization and sample complexity; Uniform Convergence; Stability; Linear predictors; Kernel Methods
• Online learning and optimization: No-regret learning; Online convex optimization and gradient descent; Online-to-batch methods; Learning from Experts; Follow-the-Leader Algorithms
• Advanced topics -- this will include theory of deep learning, and possibly topics such as advanced optimization algorithms for learning problems; learning with information and computation constraints; and learning on distributed systems.

Upon successful completion of this course students should be able to:

1. Describe basic concepts, principles and algorithms in the field of machine learning.
2. Apply their acquired knowledge of machine learning methods and principles in their own areas of research.