Course description: This course will cover fundamental
topics in Machine Learning and Data Science, including powerful
algorithms with provable guarantees for making sense of and
generalizing from large amounts of data. The course will start by
providing a basic arsenal of useful statistical and computational
tools, including generalization guarantees, core algorithmic
methods, and fundamental analysis models. We will examine
questions such as: Under what conditions can we hope to
meaningfully generalize from limited data? How can we best combine
different kinds of information such as labeled and unlabeled data,
leverage multiple related learning tasks, or leverage multiple
types of features? What can we prove about methods for summarizing
and making sense of massive datasets, especially under limited
memory? We will also examine other important constraints and
resources in data science including privacy, communication, and
taking advantage of limited interaction. In addressing these and
related questions we will make connections to statistics,
algorithms, linear algebra, complexity theory, information theory,
optimization, game theory, and empirical machine learning
research. [More info] [People and office hours]
You can
take the test in any 24-hour period you want up unil Fri Dec 18
(i.e., midnight Dec 18 is the latest hand-in date).
Here is the take-home final.
