Introduction to Online Learning
CSCI 699, Fall 2017
Haipeng Luo
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When: TuTh 2:00-3:50
Where: SGM 601 Office Hours: By appointment TA: Chen-Yu Wei (chenyu dot wei at usc dot edu) |
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Overview: This course focuses on the foundation and advances of the theory of online learning/online convex optimization/sequential decision making, which has been playing a crucial role in machine learning and many real-life applications. The main theme of the course is to study algorithms whose goal is to minimize "regret" when facing against a possibly adversarial environment, and to understand their theoretical guarantees. Special attention will be paid to more adaptive, efficient and practical algorithms. Some connections to game theory, boosting and other learning problems will also be covered. Learning Objectives: At a high-level, through this course you will have a concrete idea of what online learning is about, what the state-of-the-art is, and what the open problems are. Specifically, you will learn about classic algorithms such as exponential weights, online mirror descent, UCB, EXP3 and more recent advanced algorithms, as well as general techniques for proving regret upper and lower bounds. The hope is that after this course you will think about machine learning in a more rigorous and principled way and have the ability to design provable and practical machine learning algorithms. Requirements:
Late homework policy: You are given 4 late days for the problem sets (no late days for the final project), to be used in integer amounts and distributed as you see fit. Additional late days will each result in a deduction of 10% of the grade of the corresponding assignment. Prerequisites: Familiarity with probability, convex analysis, calculus, and analysis of algorithms. Some basic understandings of machine learning would be very helpful. Readings: There is no official textbook for this course, but the following books/surveys are very helpful in general:
Schedule:
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