CS 598

Spring 2020 Part of Term 1

Part of Term 1
Jan 21-May 6

Special Topics

SHOW FINAL EXAM SCHEDULE

Credit: 2 TO 4 hours.

Subject offerings of new and developing areas of knowledge in computer science intended to augment the existing curriculum. See Class Schedule or departmental course information for topics and prerequisites.

May be repeated in the same or separate terms if topics vary.

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CS 598 class schedule data for spring 2020
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
43815	Lecture-Discussion	CCI	9:30AM -10:45AM	WF	Siebel Center for Comp Sci	Chekuri, C	Part of Term: 1 Date Range: 01/21/20-05/06/20 Credit: 4 hours Section Title: Topics in Graph Algorithms Section Info: The course will cover a selection of algorithms and structural results for graphs that have been developed via techniques from spectral graph theory, semi-definite programming, and other linear algebraic methods. Restriction(s): Restricted to Graduate - Urbana-Champaign.
67944	Online	DM1	ARRANGED	n.a.	n.a.	Farivar, R	Part of Term: 1 Date Range: 01/21/20-05/06/20 Credit: 4 hours Section Title: Data Mining Capstone Section Info: This course is only for students that are in the Computer Science Online MCS Program. ProctorU fees may apply.. Pre-requisites: CS 410 and CS 412 Restriction(s): Restricted to Graduate - Urbana-Champaign. Restricted to MCS:Computer Sci Online -UIUC, MCS:Computer Sci Online -UIUC, or NDEG:Computer Science Onl-UIUC.
65866	Online	DSO	ARRANGED	n.a.	n.a.	Park, T	Part of Term: 1 Date Range: 01/21/20-05/06/20 Credit: 4 hours Section Title: Advanced Bayesian Modeling Section Info: This course is only for students that are in the Computer Science Online MCS Program. ProctorU fees may apply. Restricted to Graduate - Urbana-Champaign. Restricted to MCS:Computer Sci Online -UIUC, MCS:Computer Sci Online -UIUC, or NDEG:Computer Science Onl-UIUC. Restriction(s): Restricted to MCS:Computer Sci Online -UIUC, MCS:Computer Sci Online -UIUC, or NDEG:Computer Science Onl-UIUC.
41496	Lecture-Discussion	EVS	3:30PM -4:45PM	TR	Siebel Center for Comp Sci	Solomonik, E	Part of Term: 1 Date Range: 01/21/20-05/06/20 Credit: 4 hours Section Title: Prov Eff Algo Num & Comb Prob Section Info: Provably Efficient Algorithms for Numerical and Combinatorial Problems- Bridging the theory and practice of algorithms requires going beyond computational complexity to more sophisticated models of computer architecture and algorithmic cost. This course covers multiple analytical techniques that quantify the efficacy of an algorithm: parallel scalability, memory traffic, interprocessor communication, and numerical stability. General representations of algorithms (dependency graphs, bilinear algorithms) will be introduced as well as techniques for communication lower bound analysis. The course will look at problems from a variety of application domains, ranging from sorting and graph algorithms to numerical solvers and optimization. Special focus will be given to tensor computations. Restriction(s): Restricted to Graduate - Urbana-Champaign.
39664	Lecture-Discussion	HJ	2:00PM -3:15PM	WF	Siebel Center for Comp Sci	Ji, H	Part of Term: 1 Date Range: 01/21/20-05/06/20 Credit: 4 hours Section Title: Knowledge-driven Nat Lang Gen Section Info: In this course we will teach machines to describe knowledge they have learned from data. We will develop a set of intelligent systems which can transform structured knowledge bases into natural language, which is an opposite direction of Information Extraction. The topics will cover both of the conventional template filling based approaches and modern neural networks based generation approaches. We will dive deep into various technical components: how to represent knowledge, how to feed knowledge into a generation model, how to evaluate generation results? We will do three project-based assignments and a final term project interesting applications including: 1. News image and video caption generation to describe entities and events 2. Generate scripts for news videos 3. Generate scientific ideas and write technical papers 4. Write technical reviews from various perspectives 5. Write a bio for all kinds of professionals 6. Write a news article about scientific discovery results and perspectives about events 7. Write a history book 8. Make Alexa smarter by feeding information from background and real-time news streams 9. Or the other way around: Generate paitings, news videos and yoga instructional videos This is an advanced graduate-level course, and the prerequisites include Natural Language Processing and Machine Learning course. Restriction(s): Restricted to Graduate - Urbana-Champaign.
65175	Lecture-Discussion	HS	12:00PM -1:15PM	WF	Siebel Center for Comp Sci	Sundaram, H	Part of Term: 1 Date Range: 01/21/20-05/06/20 Credit: 4 hours Section Title: Advanced Social & Information Section Info: This is a deep dive into classic and recent, exciting results in network analysis, with particular emphasis on behavioral models. We shall discuss mechanism design, ideas from behavioral economics, and strategic behavior on networks. Restriction(s): Restricted to Graduate - Urbana-Champaign.
31666	Lecture-Discussion	HT	9:30AM -10:45AM	TR	Siebel Center for Comp Sci	Tong, H	Part of Term: 1 Date Range: 01/21/20-05/06/20 Credit: 4 hours Section Title: Network Mining Section Info: Networks and graphs not only appear in many high-impact application domains, but also have become an indispensable ingredient in a variety of data mining and machine learning problems. In this course, we will introduce a number of advanced topics in network mining, including network ranking, community detection, anomaly detection, network connectivity optimization, network embedding, multi-network mining, etc. Restriction(s): Restricted to Graduate - Urbana-Champaign.
69011	Lecture-Discussion	MEB	2:00PM -3:15PM	TR	Siebel Center for Comp Sci	El-Kebir, M	Part of Term: 1 Date Range: 01/21/20-05/06/20 Credit: 4 hours Section Title: Computational Cancer Genomics Section Info: Title: Computational cancer genomics This course focuses on recent algorithmic methods in cancer genomics, including somatic variant calling, phylogeny inference and identification of driver mutations. Students will study the underlying principles of these methods and the application of these methods to cancer genomics data. This course is appropriate for graduate students in computer science, bioengineering, mathematics and statistics. Familiarity with basic statistics, probability and algorithms is expected. Restriction(s): Restricted to Graduate - Urbana-Champaign.
39670	Lecture-Discussion	OOK	2:00PM -3:15PM	TR	Siebel Center for Comp Sci	Koyejo, O	Part of Term: 1 Date Range: 01/21/20-05/06/20 Credit: 4 hours Section Title: Probabilistic Graphical Models Restriction(s): Restricted to Graduate - Urbana-Champaign.
31663	Lecture-Discussion	SHP	2:00PM -3:15PM	TR	Siebel Center for Comp Sci	Har-Peled, S	Part of Term: 1 Date Range: 01/21/20-05/06/20 Credit: 4 hours Section Title: fixed-parameter tractable algo Restriction(s): Restricted to Graduate - Urbana-Champaign.
48261	Lecture-Discussion	SVA	12:30PM -1:45PM	WF	Siebel Center for Comp Sci	Adve, S	Part of Term: 1 Date Range: 01/21/20-05/06/20 Credit: 4 hours Section Title: App-Cust Heterogeneous Systems Section Info: Application-Customized Heterogeneous Systems Hardware design is evolving towards integrating multiple accelerators (IP components) to obtain application-customized systems. These components will likely be connected with a deep communication hierarchy spanning components on a single chip to within the cloud. Design methods that allow seamless integration of such components will be critical to sustainably achieving cost and performance goals for new applications. This course will cover the hardware and software challenges and application drivers of such heterogeneous system design. Topics will include accelerator architectures, heterogeneous memory and communication systems, scheduling, programming (e.g., domain specific languages and frameworks), the hardware-software interface (e.g., virtual instruction sets), and requirements of several application domains (e.g., virtual reality, machine learning, robotics, graph analytics, and human-centric computing). Students will be required to present and critique research papers and perform a substantial team project. Pre-requisites: CS 433 or equivalent or permission of the instructor. Restriction(s): Restricted to Graduate - Urbana-Champaign.