Course Catalog

Browse course information with ratings, reviews and workload estimates.

Course #	Course Name	Instructor	Overall Rating ↓	Difficulty	Workload	Professor	Lecture	Semesters	Reviews
DSC 385	Principles of Data Science	Roger Peng	5.0 / 5	2.5 / 5	9.0 hrs/wk	5.0 / 5	5.0 / 5	Spring	2
DSC 395T	Data Science for Health Discovery and Innovation	Layla Parast	4.7 / 5	2.0 / 5	5.4 hrs/wk	4.9 / 5	4.8 / 5	Spring	7
CS 395T	Advances in Deep Learning	Philipp Krähenbühl	4.6 / 5	2.8 / 5	11.2 hrs/wk	4.8 / 5	4.8 / 5	Spring, Summer	6
DSC 385	Data Exploration & Visualization	Claus O. Wilke	4.5 / 5	1.2 / 5	5.0 hrs/wk	4.9 / 5	4.8 / 5	Spring, Fall	26
CS 388	Natural Language Processing	Greg Durrett	4.5 / 5	3.4 / 5	12.5 hrs/wk	4.5 / 5	4.1 / 5	Spring, Fall	33
CS 380P	Parallel Systems	Calvin Lin & Chris Rossbach	4.5 / 5	3.9 / 5	23.0 hrs/wk	2.4 / 5	2.3 / 5	Fall	32
CS 395T	Introduction to Quantum Information Science	Scott Aaronson	4.4 / 5	4.6 / 5	21.1 hrs/wk	4.7 / 5	4.7 / 5	Spring	10
ICD10	AI in Healthcare	Ying Ding	4.3 / 5	2.4 / 5	7.5 hrs/wk	3.4 / 5	2.9 / 5	Spring, Summer, Fall	3
Unknown	Ethics in AI	Ken Fleischmann	4.3 / 5	1.4 / 5	5.0 hrs/wk	4.7 / 5	4.7 / 5	Spring, Fall	2
CS 698A/B	Thesis Option	Varies Based On Student's Area Of Interest	4.3 / 5	4.7 / 5	30.0 hrs/wk	3.2 / 5	2.9 / 5	Summer, Fall	2
CS 394D	Deep Learning	Philipp Krähenbühl	4.1 / 5	3.3 / 5	15.6 hrs/wk	2.9 / 5	2.9 / 5	Spring, Summer, Fall	67
CS 395T	Android Programming	Emmett Witchel	3.8 / 5	2.1 / 5	11.3 hrs/wk	2.9 / 5	2.1 / 5	Spring, Fall	35
DSC 395T	Data Structures & Algorithms	Calvin Lin	3.8 / 5	3.7 / 5	15.4 hrs/wk	4.0 / 5	3.8 / 5	Spring, Summer, Fall	36
CS 395T	Implementation of Programming Languages	Siddhartha Chatterjee	3.7 / 5	3.2 / 5	13.8 hrs/wk	3.9 / 5	3.8 / 5	Spring, Summer, Fall	14
CS 383C	Advanced Linear Algebra for Computing	Maggie Myers & Robert Van De Geijn	3.7 / 5	3.2 / 5	14.4 hrs/wk	2.3 / 5	3.0 / 5	Spring, Summer, Fall	41
CS 395T	Virtualization	Vijay Chidambaram	3.6 / 5	2.8 / 5	8.9 hrs/wk	3.9 / 5	3.7 / 5	Fall	11
DSC 381	Probability & Inference	Mary Parker & Peter Müller	3.5 / 5	2.7 / 5	7.9 hrs/wk	3.6 / 5	3.1 / 5	Spring, Fall	40
CS 389L	Automated Logical Reasoning	IŞIL DILLIG	3.3 / 5	4.2 / 5	23.2 hrs/wk	3.9 / 5	3.6 / 5	Spring, Fall	12
CS 391L	Machine Learning	Adam Klivans & Qiang Liu	3.3 / 5	3.5 / 5	13.1 hrs/wk	2.5 / 5	2.5 / 5	Spring, Summer, Fall	71
CS 380L	Advanced Operating Systems	Vijay Chidambaram	3.2 / 5	3.3 / 5	14.0 hrs/wk	2.2 / 5	2.1 / 5	Spring, Summer, Fall	43
DSC 383	Advanced Predictive Models	Catherine Calder & Purnamrita Sarkar	3.1 / 5	2.6 / 5	7.7 hrs/wk	3.0 / 5	3.1 / 5	Spring, Summer	11
CS 388U	Planning, Search, and Reasoning Under Uncertainty	Joydeep Biswas	3.1 / 5	1.8 / 5	6.0 hrs/wk	3.0 / 5	2.8 / 5	Spring, Fall	11
CS 394R	Reinforcement Learning	Peter Stone & Scott Niekum	3.0 / 5	3.0 / 5	10.9 hrs/wk	1.4 / 5	1.3 / 5	Spring, Summer, Fall	42
DSC 384	Design Principles & Causal Inference	Corwin Zigler	2.7 / 5	3.1 / 5	10.8 hrs/wk	3.4 / 5	3.7 / 5	Summer, Fall	17
CS 388G	Algorithms: Techniques and Theory	Vijaya Ramachandran & Greg Plaxton	2.6 / 5	4.7 / 5	22.8 hrs/wk	1.9 / 5	1.9 / 5	Spring	24
CS 395T	Online Learning and Optimization	Constantine Caramanis & Sanjay Shakkottai	2.6 / 5	2.9 / 5	10.8 hrs/wk	2.9 / 5	2.8 / 5	Spring	29
CS 400T	Case Studies in Machine Learning	Junfeng Jiao	2.5 / 5	1.3 / 5	4.0 hrs/wk	2.6 / 5	2.3 / 5	Fall	26
CS 395T	Optimization	Sujay Sanghavi & Constantine Caramanis	2.3 / 5	3.2 / 5	10.1 hrs/wk	2.1 / 5	1.9 / 5	Spring, Fall	27
DSC 382	Regression & Predictive Modeling	Stephen Walker	1.8 / 5	2.5 / 5	7.9 hrs/wk	1.9 / 5	1.7 / 5	Spring, Summer, Fall	29
CS 395T	Generative Modeling	Qiang Liu	0.0 / 5	0.0 / 5	0.0 hrs/wk	0.0 / 5	0.0 / 5	Spring, Fall	0