P. Ragde

    600 - 081 Fundamentals of CS for DS
T. Ozsu     638 - 081 Principles of Data Mgmt & Use

R. Trefler

Open background

SE

745 - 001

Computer-Aided Verification

S. Al-Kiswany

Open background
 

SN

 754 - 001

Advanced Distributed Systems

G. Kamath The prerequisites are an undergraduate level of algorithms, mathematical maturity, and exposure to probability. AC 761 - 001 Randomized Algorithms

J. Watrous

CS 768 Quantum Information Processing or equivalent

QIC

766 - 001

Theory of Quantum Information

R. Cleve

 

QIC

768 - 001

Quantum Information Processing

H. Desterck

A basic UG course on numerical methods and some experience in Python programming are strongly recommended. Students who have previously taken two or more courses on numerical methods, including a course on numerical linear algebra, are not recommended to take this course

Similar UG courses are: CS 370, CS 371, AMATH 242, CS 335, ECE 204, CS 475, CS 476

SSC 770 - 001 Numerical Analysis
K. Fountoulakis Knowledge of linear algebra, multivariate calculus, basic analysis (convergence, limits) basic probability (common distributions, means, and so on). Knowledge of programming in one of Python or Matlab. COS 794 - 001 Optimization for Data Science
J. Geelen     795 - 001 Fundamentals of Optimization
M. Grossman Open background AI 798 - 001 Advanced Research Topics: Artificial Intelligence: Law, Ethics and Policy

T. Brown

Data structures, C/C++ systems programming; concurrent and parallel programming helpful HS/ AC

798 - 002

Advanced Research Topics: Multicore Programming and Concurrent Data Structures
P. Ragde Open background PLG 842 - 001 Advanced Topics in Language Design and Implementation: Dependent Types and Software Verification
M. Godfrey Basic CS undergrad background (including upper year systems courses) and/or professional software development experience SE 846 - 001 Advanced Topics in Software Engineering: Empirical Software Evolution
K. Daudjee Open background DB 848 - 001 Advanced Topics in Data Bases: Data Infrastructure
X. He The course is open to interested graduate students with sufficient mathematical maturity. Basic knowledge in algorithms, proof techniques, and probability will be assumed. Familiarity with databases and machine learning would help but is not necessary. DB 848 - 002 Advanced Topics in Data Bases: Privacy and Fairness in Data Science
O. Abari Open background HS 854 - 001 Advanced Topics in Computer Systems: loT and Intelligent Connectivity
F. Kerschbaum An introductory course in security, privacy and cryptography AI/ HS 858 - 001 Security and Privacy for AI and Machine Learning
S. Ben-David Open background AC 860 - 001 Advanced Topics in Algorithms and Complexity:Quantum Lower Bounds
B. Ma Open background BI 882 - 001 Advanced Topics in Bioinformatics: Machine Learning in Computational Proteomics
M. McGuire Previous course in computer graphics. CG 888 - 001 Advanced Rendering Seminar
R. Cohen Open background   898 - 001 Technological Solutions to Social Problems of Computers