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: Introduction to the Theory of Computation Sipser (3rd edition)
Assessments: TBA
Prerequisites: CS 124 and 253.
Section Location Meeting Time Instructor Enrollment (max)
1 WH 112 TuTh      11:30AM - 12:45PM Michelangelo Grigni 5
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: The elements of statistical learning. Hastie, Tibshirani & Friedman. Python Machine Learning. Raschka & Mirjalili
Assessments: The grade assignment will be based on: Participation in class and discussions (10%); Three projects (20% each); Final project (30%). The instructor will assign a dataset.
Prerequisites: BIOS 500 or equivalent, multivariate calculus, linear algebra, or permission by the instructor.
Section Location Meeting Time Instructor Enrollment (max)
1 MSC W303 MW      1:00PM - 2:15PM Lee Cooper 30
2 Same as BIOS 534 TuTh      4:00PM - 5:20PM Tianwei Yu 20
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 10
CS570 Data Mining Credits: 3
Content: This course focuses on data mining concepts and techniques. Topics include: data preprocessing, data mining algorithms and methods including association analysis, classification, cluster analysis, as well as emerging applications and trends in data mining.
Texts: Data Mining: Concepts and Techniques, Third Edition. Jiawei Han, Micheline Kamber, Jian Pei
Assessments: There will be one open-book midterm exam and no final exam.
Prerequisites: Graduate Machine Learning (CS 534).
Section Location Meeting Time Instructor Enrollment (max)
1 MW      10:00AM - 11:15AM 0
CS571 Natural Language Processing Credits: 3
Content: This course covers natural language processing tasks such as part-of-speech tagging, dependency parsing, named entity recognition, coreference resolution, and sentiment analysis as well as machine learning algorithms such as adaptive gradient descent, structured prediction, clustering, and neural networks. Advanced topics such as abstract meaning representation, word embeddings, and deep learning are also discussed.
Texts: None
Assessments: Homework assignments: 40%. Paper presentation: 10%. Project proposal: 20%. Final project: 30%.
Prerequisites: Graduate Machine Learning (CS 534) or equivalent level of artificial intelligence, machine learning, natural language processing, information retrieval.
Section Location Meeting Time Instructor Enrollment (max)
1 MSC W301 MW      11:30AM - 12:45PM Jinho Choi 40
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: TBA
Assessments: TBA
Prerequisites: TBA
Section Location Meeting Time Instructor Enrollment (max)
1 MSC E408 TuTh      2:30PM - 3:45PM Ken Mandelberg 8
CS581 High Performance Computing: Tools and Applications Credits: 3
Content: Covers fundamental concepts of parallel programming, concurrent computing and network architectures, frameworks such as OpenMP and MPI. Performance monitoring, load balancing and communication will be addressed. MapReduce and Apache Spark and/or Stream Programming techniques will be discussed.
Texts: TBA
Assessments: TBA
Prerequisites: This class will have a significant implementation component. A high-level of comfort with C and Python is expected, as is working knowledge of Linux.
Section Location Meeting Time Instructor Enrollment (max)
1 WMB 4004 MW      10:00AM - 11:15AM Ashish Sharma 20
CS584 Topics in Computer Science: Advanced Systems Credits: 3
Content: This course will cover seminal recent research papers across topics in distributed computer systems, with a focus on managing big data. Topics may include communication paradigms, process management, naming, synchronization, consistency and replication, fault tolerance, storage architectures, high-performance file systems, data provenance, and next-generation storage devices and architectures, including those at Google, Yahoo, and Amazon. Throughout the course, we will discuss the tradeoffs made between performance, reliability, scalability, robustness, and security.
Texts: TBA
Assessments: TBA
Prerequisites: TBA
Section Location Meeting Time Instructor Enrollment (max)
3 MSC E408 TuTh      11:30AM - 12:45PM Avani Wildani 16
CS584 Topics in Computer Science: Intelligent Assistants Credits: 3
Content: Applications such as the Apple's Siri, Google Now, and Amazon's Alexa rely on decades of research in Information Retrieval and Natural Language processing, and more broadly AI and Human-Computer Interaction. This course will focus on both classical works in this area and the current state of the art. The course will be based largely on research papers and focused around a group project on one of the current Question Answering challenges.?
Texts: TBA
Assessments: TBA
Prerequisites: TBA
Section Location Meeting Time Instructor Enrollment (max)
2 MSC W301 TuTh      1:00PM - 2:15PM Eugene Agichtein 30
CS584 Topics in Computer Science: Time Series Analysis and Modeling Credits: 3
Content: This course will provide an introduction to basic and advanced concepts and methodologies in time-series analysis and modelling. In the first part of the course, we will cover time-domain and spectral-domain analysis techniques for uni-variate and multi-variate time series. In the second part we will cover time-series modeling and prediction using the linear and the non-linear techniques. The analysis and modelling will be performed using Matlab or the freely available scientific computing packages in Python.
Texts: Mike Cohen, “Analyzing Neural Time Series Data: Theory and Practice” 2014, MIT Press Simon Haykin, “Adaptive Filter Theory” 2013, Pearson James Hamilton, “Time Series Analysis” 1994, Princeton University Press
Assessments: Homeworks 20% Project proposal presentation and report 30% Final project presentation and report 50%
Prerequisites: Basic programming skills in a scientific computing language such as Matlab or Python.
Section Location Meeting Time Instructor Enrollment (max)
4 WMB 4004 MW      11:30AM - 12:45PM Babak Mahmoudi 15
CS584 Topics in Computer Science: The Structure of Information Networks Credits: 3
Content: The course investigates how the social, technological, and natural worlds are connected, and how the study of graphs and networks sheds light on these connections. Particular topics include: how opinions, fads, and political movements spread through society, and the technology, economics, and politics of Web information and online communities. Special emphasis will be placed on PageRank and web link analysis, the theory behind strong and weak ties in relationships, and the small-world phenomenon. Students will learn to use models and theory to explain and exploit the structure of information and social networks. Some basic toolkits for analyzing such networks will also be discussed.
Texts: TBA
Assessments: TBA
Prerequisites: TBA
Section Location Meeting Time Instructor Enrollment (max)
1 MSC W301 MW      2:30PM - 3:45PM Ymir Vigfusson 15
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      10:30AM - 12:15PM Dorian Arnold 50