Course Atlas

Graduate CS Courses

CS524 Theory Of Computing Credits: 3
Content: This course gives mathematical methods to classify the complexity of computational problems. Topics include regular languages, grammars, decidability, NP-completeness, and corresponding models of computation.
Texts: TBA
Assessments: TBA
Prerequisites: CS 124 and 253.
Section Location Meeting Time Instructor Enrollment (max)
1 MSC W301 MW      1:00PM - 2:15PM Michelangelo Grigni
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 MSC W201 TuTh      4:00PM - 5:15PM Tianwei Yu
CS554 Database Systems Credits: 3
Content: TBA
Texts: TBA
Assessments: TBA
Prerequisites: TBA
Section Location Meeting Time Instructor Enrollment (max)
1 MSC W303 TuTh      10:00AM - 11:15AM Shun Yan Cheung
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 MSC W301 TuTh      11:30AM - 12:45PM Eugene Agichtein
CS559 Distributed Processing Credits: 3
Content: This course will cover fundamental topics in Distributed Processing Systems including Synchronization (Coordination, Consensus and Consistency), Communication, Naming, Distributed Algorithms, Distributed Data Structures and Distributed System Architectures. Important cross-cutting topics include Performance, Scalability, Security and Dependability.
Texts: TBA
Assessments: TBA
Prerequisites: A Systems Programming, Operating Systems or Networking Course (CS450, CS452, CS450 or CS551) OR instructor consent.
Section Location Meeting Time Instructor Enrollment (max)
1 MSC W303 MW      2:30PM - 3:45PM Dorian Arnold
CS572 Information Retrieval Credits: 3
Content: This course will cover fundamental techniques for text-based information systems: text indexing; Boolean, vector space, and probabilistic retrieval models; structured and semi-structured search; evaluation, feedback and interface issues. Web search including algorithmic and system issues for crawling, link-based algorithms, web usage mining, and Web metadata. The goal of the course is to be exposed to current issues and trends in information retrieval and web search and to understand the fundamental algorithms behind modern web search engines.
Texts: Text: Introduction to Information Retrieval, C.D. Manning, P. RAghavan, and H. Schtze. Cambridge University Press, 2007.
Assessments: TBA
Prerequisites: Required: Proficiency in Java programming, basic probability and statistics, CS 253 or equivalent.
Section Location Meeting Time Instructor Enrollment (max)
1 MSC W303 TuTh      1:00PM - 2:15PM Eugene Agichtein
CS580 Operating Systems Credits: 3
Content: The structure and organization of computer operating systems. Process, memory, and I/O management; device drivers, inter-machine communication, introduction to multiprocessor systems. An important portion of the course is a course long programming project that implements a simple operating system in stages. Each stage takes about three weeks, and is used as a basis for the next stage.
Texts: Operating System Concepts by Silbershatz ISBN: 9781118063330
Assessments: TBA
Prerequisites: TBA
Section Location Meeting Time Instructor Enrollment (max)
1 MSC E408 TuTh      2:30PM - 3:45PM Ken Mandelberg
CS584 Topics in Computer Science: Numerical Methods for Deep Learning Credits: 3
Content: This course provides students with the mathematical background needed to analyze and further develop numerical methods at the heart of deep learning. The course briefly reviews current trends in machine learning, classification, and in particular deep neural networks. Mathematical techniques covered in this class include numerical optimization, numerical differential equations, optimal control.
Texts: Homework and additional course material will be made available using Canvas.
Assessments: TBA
Prerequisites: In order to succeed in this class, students need to have a solid background in multivariate calculus and linear algebra and some programming experience in MATLAB, Julia, or Python. In addition, students are also expected to have experience or skills in either numerical analysis (optimization, partial differential equations) or machine learning (e.g., CS534, CS584, or similar).
Section Location Meeting Time Instructor Enrollment (max)
1 MSC E406 TuTh      1:00PM - 2:15PM Lars Ruthotto
CS584 Topics in Computer Science: mHealth: Affordable and Sustainable Healthcare Technologies Credits: 3
Content: TBA
Texts: TBA
Assessments: TBA
Prerequisites: TBA
Section Location Meeting Time Instructor Enrollment (max)
1 Woodruff Memorial Bldg. 4100 Th      2:00PM - 4:45PM Gari Clifford
CS700R Graduate Seminar Credits: 1
Content: This is a required course for all students in the PhD program. It comprises seminars given by faculty, invited guests, and students.
Texts: TBA
Assessments: TBA
Prerequisites: TBA
Section Location Meeting Time Instructor Enrollment (max)
1 MSC W201 F      3:00PM - 3:50PM Eugene Agichtein