Effective Algorithms in Machine Learning and Statistics (CSCI599)

Spring 2016

This course is a graduate introduction to the algorithmic aspects of machine learning and statistics. Our focus will be on the design and analysis of statistically and computationally efficient inference algorithms in well-defined learning models.

The first part of the course will survey recent algorithmic techniques in the context of unsupervised learning. The goal of unsupervised learning is to discover hidden structure in a set of unlabeled data. We will study algorithms for learning probability distributions in various settings (shape restricted distributions, mixture models, latent variable models, topic models).

The second part of the course will focus on algorithmic problems in supervised learning. The goal of supervised learning is to infer a function from a set of labeled observations. More specifically, we will study algorithms for learning Boolean functions from labeled examples in a number of models (online learning, PAC learning, SQ learning, learning with noise, etc.).

The course is primarily targeted toward graduate students who want to gain familiarity with mathematical and algorithmic tools for the analysis of large datasets. Advanced undergraduate students with sufficient mathematical maturity are welcome (subject to instructor's approval).

Course Information


Mathematical maturity. Solid background in algorithms, linear algebra, and probability.

Course Outline

Here is a rough outline of the course material:


Course Evaluation

Homework Assignments: There will be 2 homework assignments will count for 30% of the grade. The assignments which will be proof-based, and are intended to be challenging. Collaboration and discussion among students is allowed, though students must write up their solutions independently.

Course Project: A major part of the course (50% of the grade) is a research project on a topic related to algorithmic aspects of learning theory. Projects can be completed individually or in groups of two students.

The goal of the project is to become an expert in an area related to the class material, and potentially contribute to the state of the art. There are two aspects to the course project. The first is a literature review: Students must decide on a topic and a list of papers, understand these papers in depth, and write a survey presentation of the results in their own words. The second aspect is to identify a research problem/direction on the chosen topic, think about it, and describe the progress they make.

Students must consult with the instructor during the first half of the course for help in forming project teams, selecting a suitable project topic, and selecting a suitable set of research papers. Students will be graded on the project proposal (10%), the progress report (10%), and the final report (20%). Each student will give a project presentation (10%) during the last week of classes.

The remaining part of the grade will be based on class participation (10%) and scribing lecture notes (10%).

Possible project topics can be found here.


Distribution Property Testing:

Learning Structured Distributions:

Statement on Academic Conduct and Support Systems

Academic Conduct

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Support Systems

A number of USC's schools provide support for students who need help with scholarly writing. Check with your advisor or program staff to find out more. Students whose primary language is not English should check with the American Language Institute http://dornsife.usc.edu/ali, which sponsors courses and workshops specifically for international graduate students. The Office of Disability Services and Programs http://sait.usc.edu/academicsupport/centerprograms/dsp/home_index.html provides certification for students with disabilities and helps arrange the relevant accommodations. If an officially declared emergency makes travel to campus infeasible, USC Emergency Information http://emergency.usc.edu/ will provide safety and other updates, including ways in which instruction will be continued by means of blackboard, teleconferencing, and other technology.