Course Atlas

Graduate CS Courses

Graduate CS Courses

CS523 Data Structure & Algorithms I Credits: 3
Content: This course introduces practical algorithms and data structures, for students entering graduate computer science from other fields of study.
Texts: TBA
Assessments: TBA
Prerequisites: The prerequisites are introductory programming and some discrete mathematics, which we expect our entering students already have. Students who have taken an undergraduate algorithms course (similar to our CS 323, and typically included in a Computer Science major) may place out.
Section Location Meeting Time Instructor Enrollment (max)
1 MSC W301 MW      4:00PM - 5:15PM Michelangelo Grigni 30
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: Introduction to Algorithms by Cormen, Leiserson, Rivest, and Stein (2009, 3rd edition)
Assessments: During the first half of the course there will be three written homeworks and midterm exam. In the second half, I’ll grade your presentation (and support documents), and I’ll also keep track of participation (attendance and interaction). Each graded item gets a mark in the range 0 to 100, curved so that the median is at least 85 (B). To get your final course average, I plan to take a weighted average of the marks, as follows: • each homework gets a weight of one, • the midterm exam gets a weight of three, • your presentation gets a weight of two, • your support documents get a weight of one, • class participation (plus any quizzes) gets a weight of one.
Prerequisites: CS 224 and CS 253.
Section Location Meeting Time Instructor Enrollment (max)
1 MSC W303 MW      1:00PM - 2:15PM 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: Required: The Elements of Statistical Learning: Data Mining, Inference, and Prediction), by Trevor Hastie, Robert Tibshirani & Jerome Friedman Supplemental: Machine Learning: a Probabilistic Perspective, by Kevin Murphy Supplemental: Pattern Recognition and Machine Learning, by Christopher Bishop
Assessments: Homeworks 35% Midterm 15% Project 40% Participation 10%
Prerequisites: Cross-listed with BIOS 534. 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 W301 TuTh      1:00PM - 2:15PM Joyce Ho 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 MSC W301 TuTh      2:30PM - 3:45PM Ken Mandelberg 20
CS554 Database Systems Credits: 3
Content: This course covers tabular data storage and processing using the classic SQL language, query processing and optimization algorithms. Novel data formats are explored emphasizing commonly used key-value, document, RDF and graph representations. Storage and processing techniques for large quantity of data are presented.
Texts: None
Assessments: TBA
Prerequisites: Undergraduate level knowledge of data structures, algorithms and systems programming.
Section Location Meeting Time Instructor Enrollment (max)
1 MSC W301 TuTh      11:30AM - 12:45PM Shun Yan Cheung 30
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 W303 TuTh      10:00AM - 11:15AM Eugene Agichtein 15
CS573 Data Privacy and Security Credits: 3
Content: This course will introduce students to the legal and ethical issues of data privacy and security and computational technologies for protecting privacy and security while allowing society to collect and share person-specific data for many worthy purposes. The main topics include privacy and anonymity models, data anonymization, privacy preserving data mining, access control, secure computations, privacy in social networks and privacy in clinical and public health research. The foundations are drawn from a number of sub-disciplines of Computer Science including: database systems, data mining, computer security, cryptography, and statistics.
Texts: There are no required textbooks.
Assessments: Assignments and presentations 40 Midterm 30 Project 30
Prerequisites: Familiarity with a programming language and basic knowledge in algorithms, database systems or data mining are required.
Section Location Meeting Time Instructor Enrollment (max)
1 MSC W303 MW      10:00AM - 11:15AM Li Xiong 20
CS584 Topics in Computer Science: Computer Security Credits: 3
Content: TBA
Texts: TBA
Assessments: TBA
Prerequisites: TBA
Section Location Meeting Time Instructor Enrollment (max)
3 MSC W301 MW      2:30PM - 3:45PM Ymir Vigfusson 10
CS584 Topics in Computer Science: Programing Language Credits: 3
Content: TBA
Texts: TBA
Assessments: TBA
Prerequisites: TBA
Section Location Meeting Time Instructor Enrollment (max)
2 MSC W303 MW      11:30AM - 12:45PM James Lu 5
CS590 Teaching Seminar Credits: 1
Content: This course explores theoretical and practical approaches for effective teaching, with particular emphasis on the discipline of Computer Science. After this course, students will be able to demonstrate knowledge of multiple pedagogical strategies, write a syllabus, develop assessment items, and design and deliver lectures and presentations for a variety of different audiences.
Texts: None
Assessments: None
Prerequisites: None
Section Location Meeting Time Instructor Enrollment (max)
1 MSC E406 F      2:00PM - 2:50PM Steven La Fleur 20
CS597R Directed Study: Professional Development Credits: 1
Content: The purpose of this course is to provide CS Masters students with the necessary career management skills to effectively identify, compete, and secure relevant career-launching internships and full-time professional career opportunities.
Texts: None
Assessments: Class Attendance (3 pts per class) 48 points Occupational Outlook Handbook Homework 10 points Gap Analysis 10 points Resumes · Resume draft to class 5 points · Resume uploaded to Handshake 5 points Professional Cover Letter 5 points LinkedIn Profile Uploaded & Complete 10 points Complete Handshake Profile 5 points Informational Interviews/Written Summaries 10 points Career Fair Reflection Paper 10 points Mock Video Interview 10 points Job Search Website Homework 5 points Professional Dress Requirements Satisfied 25 points Final Project: Portfolio 50 points Total possible 208 points
Prerequisites: None
Section Location Meeting Time Instructor Enrollment (max)
1 MSC W301 F      2:00PM - 3:00PM Paul Fowler
Shun Yan Cheung
30
CS597R Directed Study Credits: 1-9
Content: The primary goal of this course is the guided development of a research proposal, based on the student's research project. Class meetings are conducted in an interactive workshop format.
Texts: None
Assessments: TBA
Prerequisites: TBA
Section Location Meeting Time Instructor Enrollment (max)
2        Ymir Vigfusson 2
CS599 CS Research Credits: 1-9
Content: TBA
Texts: TBA
Assessments: TBA
Prerequisites: TBA
Section Location Meeting Time Instructor Enrollment (max)
1        Jinho Choi 5
2        Dorian Arnold 5
3        Gari Clifford 5
4        Lee Cooper 5
5        Li Xiong 5
6        Joyce Ho 5
7        Ymir Vigfusson 5
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: None
Assessments: None
Prerequisites: None
Section Location Meeting Time Instructor Enrollment (max)
1 MSC W201 F      3:00PM - 4:00PM Dorian Arnold 30