Course Atlas

Graduate CS Courses

CS526 Algorithms Credits: 3
Content: This course is a graduate level introduction to the design and analysis of algorithms. Although we will review some undergraduate level material, we will instead emphasize reading and experimentation at a level appropriate for the initiation of research. This course will have both theoretical and practical content. As course highlights, students will be expected to implement and analyze the performance of a fundamental data structure, starting with a close reading of the original research paper.
Texts: TBA
Assessments: TBA
Prerequisites: CS 224 and CS 253.
Section Location Meeting Time Instructor Enrollment (max)
1 ONLINE TuTh      9:40AM - 10:55AM Michelangelo Grigni 30
CS534 Machine Learning Credits: 3
Content: This course covers fundamental machine learning theory and techniques. The topics include basic theory, classification methods, model generalization, clustering, and dimension reduction. The material will be conveyed by a series of lectures, homeworks, and projects.
Texts: TBA
Assessments: TBA
Prerequisites: Knowledge of linear algebra, multivariate calculus, basic statistics and probability theory. Homework and project will require programming in Python, Matlab, C/C++ or R. Or permission by the instructor.
Section Location Meeting Time Instructor Enrollment (max)
1 ONLINE MW      9:40AM - 10:55AM Yubin Park 30
CS551 Systems Programming Credits: 3
Content: Systems programming topics will be illustrated by use of the Unix operating system. Topics include: file i/o, the tty driver, window systems, processes, shared memory, message passing, semaphores, signals, interrupt handlers, network programming and remote procedure calls. Programming examples and assignments will illustrate the system interface on actual computer hardware. All assignments will be in written in C. The department's computing lab will be used in the course to allow students to get hands-on experience with operating system and hardware topics that cannot effectively be pursued on a central timesharing computer.
Texts: TBA
Assessments: TBA
Prerequisites: TBA
Section Location Meeting Time Instructor Enrollment (max)
1 ONLINE TuTh      2:40PM - 3:55PM Ken Mandelberg 40
CS557 Artificial Intelligence Credits: 3
Content: This course covers core areas of Artificial Intelligence including perception, optimization, reasoning, learning, planning, decision--making, knowledge representation, vision and robotics.
Texts: TBA
Assessments: TBA
Prerequisites: Undergraduate level of Artificial Intelligence or Machine Learning.
Section Location Meeting Time Instructor Enrollment (max)
1 ONLINE MW      11:20AM - 12:35PM James Lu 30
CS584 Topics in Computer Science: Structure of Information Networks Credits: 3
Content: This course will explore the fundamentals of Quantum Computing. Quantum computers have the potential to efficiently solve certain problems that are intractable for traditional classical computers. Topics include: fundamental models of quantum computing, reversible computing, qubits, entanglement and non-locality, quantum protocols, quantum circuits; simple quantum algorithms, quantum Fourier transform, Shor factoring algorithm, Grover search algorithm, quantum error correction.
Texts: TBA
Assessments: TBA
Prerequisites: Equivalent of CS 326 Analysis of Algorithms and Linear Algebra (such as Math 221)
Section Location Meeting Time Instructor Enrollment (max)
1 ONLINE TuTh      1:00PM - 2:15PM Ymir Vigfusson 15
CS584 Topics in Computer Science Credits: 3
Content: TBA
Texts: TBA
Assessments: TBA
Prerequisites: TBA
Section Location Meeting Time Instructor Enrollment (max)
2 ONLINE MW      2:40PM - 3:55PM Carl Yang 30
CS584 Topics in Computer Science: TBA Credits: 3
Content: TBA
Texts: TBA
Assessments: TBA
Prerequisites: TBA
Section Location Meeting Time Instructor Enrollment (max)
4 ONLINE TuTh      4:20PM - 5:35PM Abeed Sarker 30