CSCI 1952Q: Algorithmic Aspects of Machine Learning (Spring 2023)

Basic Information

Course Description

In this course, we will explore the theoretical foundations of machine learning and deep learning. We will focus on designing and analyzing machine learning algorithms with provable guarantees. More specifically, in this course we will (1) introduce basic tools in linear algebra and optimization, including the power method, singular value decomposition, matrix calculus, (matrix) concentration inequalities, and (stochastic) gradient descent, (2) cover many examples where one can design algorithms with provably guarantees for fundamental problems in machine learning (under certain assumptions), including topic modeling, tensor decomposition, sparse coding, and matrix completion, and (3) discuss the emerging theory of deep learning, including landscape analysis, generalization and over-parameterization, neural tangent kernels, generalization bounds, and implicit regularization.

Top Projects and Reviewers

The course uses a voluntary peer-review process to evaluate all final project submissions. Based on this peer-review process, the following projects/students are chosen as top projects/reviewers:


Warning: the following assignments and due dates are from the past. If you are taking CSCI 1952Q this semester, please refer to the current course webpage.

Course Project



Disability Policies

Brown University is committed to full inclusion of all students. Any student with a documented disability is welcome to contact the instructor as early in the semester as possible so that reasonable accommodations can be arranged. If you need accommodations around online learning or in-classroom accommodations, please be sure to reach out to Student Accessibility Services (SAS) for their assistance (, 401-863-9588, Brown SAS Website). Students may also speak with Student Accessibility Services at 401-863-9588 to discuss the process for requesting accommodations.

Religious Holidays

Students who wish to observe their religious holidays shall inform the instructor within the first four weeks of the semester, unless the religious holiday is observed in the first four weeks. In such cases, the students shall notify the faculty member at least five days in advance of the date when he/she will be absent. The instructor shall make every reasonable effort to honor the request.