CS 598

Fall 2019 Part of Term 1

Part of Term 1
Aug 26-Dec 11

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 fall 2019
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
69375	Online	AO2	ARRANGED	n.a.	n.a.	Renear, A	Part of Term: 1 Date Range: 08/26/19-12/11/19 Credit: 4 hours Section Title: Foundations of Data Curation Section Info: This course is only for students that are in the online Computer Science MCS/MCS-DS Program. Additional ProctorU fees may apply. 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.
64616	Lecture-Discussion	APK	2:00PM -3:15PM	WF	Siebel Center for Comp Sci	Kloeckner, A	Part of Term: 1 Date Range: 08/26/19-12/11/19 Credit: 4 hours Section Title: Fast Algorithms & Intrgl Equat Section Info: Fast Algorithms & Integral Equations Near-linear-complexity ("fast") numerical algorithms and related numerical methods, mainly for the numerical solution of elliptic partial differential equations, such as Laplace, Helmholtz, Stokes, Maxwell's, or elasticity. Numerical rank, complexity/accuracy trade-offs, notions of convergence. Multi-level compression schemes. Tree codes, Fast Multipole Methods. Potential Theory and Integral Equations. Quadrature. Fast, compression-based, linear-time direct solvers based, randomized linear algebra. Fast function transforms: Uniform and non-uniform FFTs, Butterfly algorithms. Prerequisites: Linear Algebra, programming experience, some exposure to Partial Differential Equations. Restriction(s): Restricted to Graduate - Urbana-Champaign.
70203	Lecture-Discussion	BL	3:30PM -4:45PM	TR	Siebel Center for Comp Sci	Li, B	Part of Term: 1 Date Range: 08/26/19-12/11/19 Credit: 4 hours Section Title: Adversarial Machine Learning Restriction(s): Restricted to Graduate - Urbana-Champaign.
49221	Online	CC1	ARRANGED	n.a.	n.a.	Farivar, R	Part of Term: 1 Date Range: 08/26/19-12/11/19 Credit: 4 hours Section Title: Cloud Computing Capstone Section Info: This course is only for students that are in the online Computer Science MCS/MCS-DS Program. Additional ProctorU fees may apply. 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.
67238	Lecture-Discussion	CLF	11:00AM -12:15PM	WF	Siebel Center for Comp Sci	Fletcher, C	Part of Term: 1 Date Range: 08/26/19-12/11/19 Credit: 4 hours Section Title: Secure Processor Design Section Info: Secure Processor Design and Foundations in Applied Cryptography With the emergence of systems such as ARM Trustzone and Intel Software Guard Extensions, secure processors have become one of the next frontiers in secure systems design. Secure processors allow emerging applications (e.g., computation outsourcing) to be realized with a significantly smaller trusted computing base and/or significantly reduced performance overheads, relative to a "pure software" solution. This course will bring students to the cutting-edge in secure processor architecture by examining the interplay between hardware, software and applied cryptography in these systems. The course day-to-day will be readings and discussion of top papers in the field. Course assignments will give students hands-on experience with the Intel Software Guard Extensions (SGX) SDK, building secure applications and evaluating their security. The end of semester will culminate in an original research project. Restriction(s): Restricted to Graduate - Urbana-Champaign.
72082	Lecture-Discussion	DK	3:30PM -4:45PM	WF	Siebel Center for Comp Sci	Khurana, D	Part of Term: 1 Date Range: 08/26/19-12/11/19 Credit: 4 hours Section Title: Special Topics in Cryptography Section Info: Cryptography, that started as the study of secret communication, has undergone a major revolution in the last few years. It now helps us realize a variety of seemingly impossible tasks: from allowing computations on secret data without revealing the data itself, to offloading computation to untrusted clients while maintaining verifiable results, and even making programs unintelligible while preserving functionality. This course will cover a selection of such cutting-edge topics in modern cryptography. We will understand how an adversary that breaks advanced protocols can be transformed into an adversary that contradicts a basic assumption such as the hardness of factoring. Our focus will be on understanding key ideas in cryptography research published over the last few years, and identifying new directions and problems for the future. This is a seminar course in Cryptography. Students will be expected to read and discuss research papers in the second part of the course. Restriction(s): Restricted to Graduate - Urbana-Champaign.
72098	Lecture-Discussion	DLT	3:30PM -4:45PM	TR	Siebel Center for Comp Sci	Telgarsky, M	Part of Term: 1 Date Range: 08/26/19-12/11/19 Credit: 4 hours Section Title: Deep Learning Theory Section Info: This course will overview deep learning theory, with a goal of providing students everything they need to consume and produce research in the field. Topics will include (but are not limited to): approximation, generalization, and optimization properties of deep networks. The course will provide very brief background in learning theory (e.g., an overview of Rademacher complexity); students are expected to have taken probability, linear algebra, and an introductory course in machine learning. Evaluation is based both on homeworks (in the first 50-70% of lectures, presented by the instructor), and on an in-depth course presentation. Restriction(s): Restricted to Graduate - Urbana-Champaign.
70878	Online	DM1	ARRANGED	n.a.	n.a.	Zhai, C	Part of Term: 1 Date Range: 08/26/19-12/11/19 Credit: 4 hours Section Title: Data Mining Capstone Restriction(s): Restricted to Computer Science or Bioinformatics major(s). Restricted to Graduate - Urbana-Champaign. Restricted to MCS:Computer Sci Online -UIUC or NDEG:Computer Science Onl-UIUC.
69343	Online	DSO	ARRANGED	n.a.	n.a.	Park, T	Part of Term: 1 Date Range: 08/26/19-12/11/19 Credit: 4 hours Section Title: Advanced Bayesian Modeling Section Info: Advanced Bayesian Modeling Description: Practical methods and models for Bayesian data analysis. Topics include Bayesian fundamentals, prior selection, posterior inference tools, hierarchical models, methods of Bayesian computation, model evaluation, and ordinary and generalized regression models. Emphasis on computational implementation. Prerequisites: STAT 420 and knowledge of R. Restricted to online MCS students. Additional ProctorU fees may apply. Non-Degree seeking students may enroll on a space-available basis with consent. To request enrollment, please complete the “Non-Degree Enrollment Request Form” here: https://illinois.edu/fb/sec/9478165 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.
36005	Lecture-Discussion	GLE	8:30AM -9:50AM	MW	Siebel Center for Comp Sci	Herman, G	Part of Term: 1 Date Range: 08/26/19-12/11/19 Credit: 4 hours Section Title: Learning & Comp. Science Topic Section Info: Learning and Computer Science Topics Computing is becoming a fundamental skill for many disciplines and is becoming pervasive. Through reading the research literature and having interactive discussions, the course will provide an overview of what we know (and what we don’t know!) about how people learn. We will apply these theories to examine how we can help students learn computing and how we can use computers to help people learn. Topics will include how people organize knowledge, create concepts, interact with visual displays, and manage cognitive load. We will also discuss how to design and perform educational research studies. The course will culminate in students writing the core of a National Science Foundation grant proposal or prototyping an educational technology. Restricted to ECE graduate students. Restriction(s): Restricted to Graduate - Urbana-Champaign.
36004	Lecture-Discussion	GLH	8:30AM -9:50AM	MW	Siebel Center for Comp Sci	Herman, G	Part of Term: 1 Date Range: 08/26/19-12/11/19 Credit: 4 hours Section Title: Learning and Comp Science Section Info: Learning and Computer Science Topics Computing is becoming a fundamental skill for many disciplines and is becoming pervasive. Through reading the research literature and having interactive discussions, the course will provide an overview of what we know (and what we don’t know!) about how people learn. We will apply these theories to examine how we can help students learn computing and how we can use computers to help people learn. Topics will include how people organize knowledge, create concepts, interact with visual displays, and manage cognitive load. We will also discuss how to design and perform educational research studies. The course will culminate in students writing the core of a National Science Foundation grant proposal or prototyping an educational technology. Restriction(s): Restricted to Graduate - Urbana-Champaign.
57780	Lecture-Discussion	GW	12:30PM -1:45PM	WF	Siebel Center for Comp Sci	Wang, G	Part of Term: 1 Date Range: 08/26/19-12/11/19 Credit: 4 hours Section Title: ML for Sys, Netwrks & Security Section Info: In recent years, machine learning has significantly extended the capabilities of data-driven methods to solve new problems in System, Networking, and Security domains. Exciting progress has been made in various machine learning applications ranging from vulnerability discovery and security defense to network protocol design, software testing, and system optimization. In this class, we will examine the most creative and “crazy” ideas of applying machine learning to solve system and security problems. The focus will be on exploring new research directions and understanding the limitations and potential risks of this approach. Students will be expected to read, present, and discuss research papers, and work on an original research project. The goal of the project is to extend machine learning techniques to new problems and produce real and publishable results. Restriction(s): Restricted to Graduate - Urbana-Champaign.
72121	Lecture-Discussion	HJ	2:00PM -3:15PM	WF	Siebel Center for Comp Sci	Ji, H	Part of Term: 1 Date Range: 08/26/19-12/11/19 Credit: 4 hours Section Title: Info Extr and Knowledge Acq Section Info: This is an advanced research-centric course to introduce the most up-to-date techniques in Information Extraction and Knowledge Acquisition, which aim to create the next generation of information access in which humans can communicate with computers in any natural language beyond keyword search, and computers can discover accurate, concise, and trustable information and knowledge embedded in big data from heterogeneous sources. We will select ten trending topics such as deep neural networks for Information Extraction, never-ending knowledge acquisition, zero-shot learning for cross-domain transfer. and give a comprehensive overview for each topic. We will review where we have been (the most successful methods in literature), and where we are going (the remaining challenges, and novel methods to tackle these challenges). The target audience of this course is PhD students who do thesis research related to these topics. We also expect to invite several top researchers in this field to give guest lectures. The goal is for each student to have at least one solid paper submission ready at the end of this course. We will select classic papers about each topic and ask students to duplicate the core algorithms and even advance state-of-the-art with new ideas. We also aim to strengthen everyone’s presentation and writing skills, so we will do peer review on the presentations and paper submissions. Restriction(s): Restricted to Graduate - Urbana-Champaign.
49222	Lecture	HPN	12:30PM -1:50PM	TR	Electrical & Computer Eng Bldg	Mittal, R	Part of Term: 1 Date Range: 08/26/19-12/11/19 Credit: 4 hours Section Title: High-Speed/Progrmable Networks Section Info: Topic: Emerging Programming Paradigms. A new generation of applications is changing the nature of programming with the need for scalability, parallelism, distribution, and mobility. Moreover, web applications require context awareness; cloud computing requires balancing availability, consistency and reliability; sensor networks use broadcast messages and have limited computational resources; and cyberphysical systems must also specify real-time control. The course will cover actor languages and related programming paradigms to address these challenges.
65089	Lecture-Discussion	JP	11:00AM -12:15PM	MW	Siebel Center for Comp Sci	Peng, J	Part of Term: 1 Date Range: 08/26/19-12/11/19 Credit: 4 hours Section Title: Machine Lrning Computation Bio Section Info: This course focuses on modern machine learning techniques in computational biology, including probabilistic modeling, feature selection, graphical models, approximate inference and learning, Monte Carlo methods and neural networks. Students will learn the development of the theoretical concepts for these methods and the applications of these methods to a variety of problems in computational biology. 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.
64618	Lecture-Discussion	JT	12:30PM -1:45PM	WF	Siebel Center for Comp Sci	Torrellas, J	Part of Term: 1 Date Range: 08/26/19-12/11/19 Credit: 4 hours Section Title: Energy-Efficient Comp Architec Section Info: Topic: Energy-Efficient Computer Architecture This course will discuss recent issues and research trends in designing computer architectures for energy efficiency. The course will start with an analysis of process variation and wear-out, which constrains and affects energy efficiency. We will examine models and techniques for variation tolerance at different levels. They include body biasing, processors with timing speculation, and variation-aware application scheduling. We will then focus on low-voltage computer architecture, which is our best hope for energy efficiency. We will examine how to reduce voltage guard-bands and manage voltage droops. Higher-level techniques include pipeline design for low voltage, efficient eDRAM refresh, extensive power gating, and effective on-chip controllers. Next, we will consider 3D architectures and how they can improve energy efficiency. Finally, we will focus on extreme-scale computer architectures, which are designed from the ground up for energy efficiency. They will bring together all of the concepts discussed in the course into a single platform. Pre-requisite courses: Required: CS433 or equivalent; Recommended: CS533 or equivalent Restriction(s): Restricted to Graduate - Urbana-Champaign.
40108	Lecture-Discussion	JTE	12:30PM -1:45PM	WF	Siebel Center for Comp Sci	Torrellas, J	Part of Term: 1 Date Range: 08/26/19-12/11/19 Credit: 4 hours Section Title: Energy-Efficient Comp Architec Section Info: Topic: Energy-Efficient Computer Architecture This course will discuss recent issues and research trends in designing computer architectures for energy efficiency. The course will start with an analysis of process variation and wear-out, which constrains and affects energy efficiency. We will examine models and techniques for variation tolerance at different levels. They include body biasing, processors with timing speculation, and variation-aware application scheduling. We will then focus on low-voltage computer architecture, which is our best hope for energy efficiency. We will examine how to reduce voltage guard-bands and manage voltage droops. Higher-level techniques include pipeline design for low voltage, efficient eDRAM refresh, extensive power gating, and effective on-chip controllers. Next, we will consider 3D architectures and how they can improve energy efficiency. Finally, we will focus on extreme-scale computer architectures, which are designed from the ground up for energy efficiency. They will bring together all of the concepts discussed in the course into a single platform. Pre-requisite courses: Required: CS433 or equivalent; Recommended: CS533 or equivalent Restricted to ECE graduate students. Restriction(s): Restricted to Graduate - Urbana-Champaign.
36002	Lecture-Discussion	KCC	9:30AM -10:45AM	MF	Siebel Center for Comp Sci	Chang, K	Part of Term: 1 Date Range: 08/26/19-12/11/19 Credit: 4 hours Section Title: Listening to Social Universe Section Info: TOPIC: With the emergence and proliferation of social media such as Twitter, Instagram, and Reddit, we are facing a sea change-- with a magnitude much like the Web revolution two decades ago-- in not only how people express themselves and communicate, but also how we can listen to the world. With their ubiquitous popularity, while social networks have connected people, these social media spread their voices, and thus thoughts and information propagate in a speed and scale unseen before-- allowing for our listening to the world with algorithms, at not only a large scale, but also a high precision. This course will study advanced social analytic techniques for discovering and profiling the online social universe. Restriction(s): Restricted to Graduate - Urbana-Champaign.
59671	Lecture-Discussion	KN	2:00PM -3:15PM	TR	Siebel Center for Comp Sci	Nahrstedt, K	Part of Term: 1 Date Range: 08/26/19-12/11/19 Credit: 4 hours Section Title: Advanced Multimedia Systems Section Info: Advanced Multimedia Systems Topic: Advanced Multimedia Systems. Multimedia data and underlying systems and networks that service multimedia (multi-modal sensory) data are becoming ubiquitous. In the "Advanced Multimedia Systems" class we will explore major advances that are made in multimedia data, systems and networks to enable next generation multimedia applications such as Skype, YouTube, Flickr and others. We will take the end-to-end approach and explore an integrated view of multimedia systems ranging from 2D and 3D video and audio, advanced compression techniques H.264, MPEG4 and MPEG-7, new multimedia transport protocols and Quality of Service, Content Distribution and Peer-to-Peer networks, multi-modal synchronization, machine learning and deep learning techniques for multi-modal data, services such as Voice-over-IP, Video Conferencing, Video-on-Demand, and subjective and objective Quality of Experiene evaluation methods for next generation multimedia applications. Restriction(s): Restricted to Graduate - Urbana-Champaign.
72122	Lecture-Discussion	LR	12:30PM -1:45PM	TR	Siebel Center for Comp Sci	Ren, L	Part of Term: 1 Date Range: 08/26/19-12/11/19 Credit: 4 hours Section Title: Consensus Algorithms Section Info: This course covers classic results and recent advances in consensus algorithms. The course studies different problem formulations including Byzantine agreement, broadcast primitives and state machine replication, different models and assumptions regarding timing, fault pattern, cryptography and setup, state-of-art algorithms and lower bounds under various combinations of these settings, common algorithm design techniques including randomization, leader election and quorum systems, Nakamoto’s new paradigm of permissionless consensus including the Bitcoin protocol, improvement proposals, alternative designs, and connections to the permissioned setting. Restriction(s): Restricted to Graduate - Urbana-Champaign.
66318	Lecture-Discussion	LRS	3:30PM -4:50PM	TR	Siebel Center for Comp Sci	Sha, L	Part of Term: 1 Date Range: 08/26/19-12/11/19 Credit: 4 hours Section Title: Improving Your Research Skills Section Info: This class aims at improving graduate students research skills including: 1) how to formulate high impact research problems; 2) how to create a research agenda and carry it out; and 3) how to give effective presentations and write papers. Students will be organized into teams and present what they have learned. For an overview, see the 2016 class website, https://wiki.illinois.edu/wiki/display/cs598lrs/Home. Restriction(s): Restricted to Graduate - Urbana-Champaign.
72109	Lecture-Discussion	LRW	12:30PM -1:45PM	TR	Siebel Center for Comp Sci	Rauchwerger, L	Part of Term: 1 Date Range: 08/26/19-12/11/19 Credit: 4 hours Section Title: Port High Perf Comp via DSL Section Info: Portable High Performance Computing means portable performance in time and space. In other words, the performance of programs requiring HPC (resource intensive codes) need to be portable across the main existing architectures and across the machines that will be developed in time. In order not to have to rewrite entire applications to port them across systems we raise the level of abstraction at which programs are written and leave the 'details" of mapping the application onto the architecture to the vendors themselves. One way to achieve portable performance of applications and ease of programming effort is to use a hierarchical system of generic and domain specific libraries. Libraries can be built on top of the existing software development infrastructure, i.e. do not require new compilers, debuggers, etc. performance Libraries have to implement parallel algorithms and programs without requiring the user to deal with the complexities of parallel program execution on a parallel architecture. In this course we first present the state of the art in parallel programming models. Then we will present the latest approaches to parallel library design and how they alleviate the performance portability and ease of use problems. We will cover generic libraries such as TBB, STAPL, Cilk++ and then spend some time on libraries for machine learning applications such as Tensorflow. Restriction(s): Restricted to Graduate - Urbana-Champaign.
72123	Lecture-Discussion	MAV	3:30PM -4:45PM	TR	Siebel Center for Comp Sci	Forsyth, D	Part of Term: 1 Date Range: 08/26/19-12/11/19 Special Approval: Instructor Approval Required Credit: 4 hours Section Title: Methods for Bld Auton Vehicles Section Info: Graduate and undergraduate student interested in taking CS 598 MAV need to attend the first class. This includes ECE students interested in the course. Restriction(s): Restricted to Graduate - Urbana-Champaign.
46989	Lecture-Discussion	PS	12:30PM -1:45PM	TR	Siebel Center for Comp Sci	Smaragdis, P	Part of Term: 1 Date Range: 08/26/19-12/11/19 Credit: 4 hours Section Title: Mach Lrng for Signal Processng Section Info: Topic: Machine Learning for Signal Processing. Prerequisite: Linear algebra, Probability theory. Today we see an increasing need for machines that can understand complex real-world signals, such as speech, images, movies, music, biological and mechanical readings, etc. In this course we will cover the fundamentals of machine learning and signal processing as they pertain to this goal, as well as exciting recent developments. We will learn how to decompose, analyze, classify, detect and consolidate signals, and examine various commonplace operations such as finding faces from camera feeds, organizing personal music collections, designing speech dialog systems and understanding movie content. The course will consist of lectures and student projects and presentations. Students are expected to have a working knowledge of linear algebra, probability theory, and programming skills to carry an implementation of a final project (preferably in Python, but all languages are welcome). Restriction(s): Restricted to Graduate - Urbana-Champaign.
36011	Lecture-Discussion	PSE	12:30PM -1:45PM	TR	Siebel Center for Comp Sci	Smaragdis, P	Part of Term: 1 Date Range: 08/26/19-12/11/19 Credit: 4 hours Section Title: Mach Lrng for Signal Processng Section Info: Topic: Machine Learning for Signal Processing. Prerequisite: Linear algebra, Probability theory. Today we see an increasing need for machines that can understand complex real-world signals, such as speech, images, movies, music, biological and mechanical readings, etc. In this course we will cover the fundamentals of machine learning and signal processing as they pertain to this goal, as well as exciting recent developments. We will learn how to decompose, analyze, classify, detect and consolidate signals, and examine various commonplace operations such as finding faces from camera feeds, organizing personal music collections, designing speech dialog systems and understanding movie content. The course will consist of lectures and student projects and presentations. Students are expected to have a working knowledge of linear algebra, probability theory, and programming skills to carry an implementation of a final project (preferably in Python, but all languages are welcome). Restricted to ECE graduate students. Restriction(s): Restricted to Graduate - Urbana-Champaign.
70683	Online	PSO	ARRANGED	n.a.	n.a.	Liang, F	Part of Term: 1 Date Range: 08/26/19-12/11/19 Credit: 4 hours Section Title: Practical Statistical Learning Restriction(s): Restricted to Graduate - Urbana-Champaign. Restricted to MCS:Computer Sci Online -UIUC or NDEG:Computer Science Onl-UIUC.
71083	Online	PSP	ARRANGED	n.a.	n.a.	Zhu, R	Part of Term: 1 Date Range: 08/26/19-12/11/19 Credit: 4 hours Section Title: Practical Statistical Learning Section Info: Additional Coursera ID verification and ProctorU fees may apply. This is a pilot course and no additional seats will be released Restriction(s): Restricted to MCS:Computer Sci Online -UIUC, MCS:Computer Sci Online -UIUC, or NDEG:Computer Science Onl-UIUC.
63395	Lecture-Discussion	RK	12:30PM -1:45PM	TR	Siebel Center for Comp Sci	Kumar, R	Part of Term: 1 Date Range: 08/26/19-12/11/19 Credit: 4 hours Section Title: HCI for ML Section Info: Explores the use of data-driven methods to support creative design processes by examining recent work in human computer-interaction, product design, cognitive science, machine learning, graphics, vision, and natural language processing. Students will read and discuss recent papers from these fields, and work in teams on a multi-week project to build data-driven tools to solve real-world design problems. Practical data mining and machine learning knowledge is emphasized: crowdsourcing and web scraping, model and feature selection, parameter tuning. The course has no formal prerequisites, but students should be algorithmically and programmatically mature. Restriction(s): Restricted to Graduate - Urbana-Champaign.
70200	Lecture-Discussion	RM	3:30PM -4:45PM	WF	Siebel Center for Comp Sci	Mehta, R	Part of Term: 1 Date Range: 08/26/19-12/11/19 Credit: 4 hours Section Title: Algorithmic Game Theory Section Info: Description: Algorithmic game theory has become more relevant than ever before with the advent of online markets, ad auctions, social networks, and recommendation systems, where rational agents interact to achieve selfish goals. The last two decades have witnessed the development of a rich theory in this area and deep mathematical connections have been established. The first half of the course will provide a broad introduction to games and market models, solution concepts, equilibrium computation & complexity, price of anarchy, auctions, and others. The second half will address a selection of advanced topics and research projects. Restriction(s): Restricted to Graduate - Urbana-Champaign.
66867	Lecture-Discussion	SM	11:00AM -12:15PM	TR	Siebel Center for Comp Sci	Misailovic, S	Part of Term: 1 Date Range: 08/26/19-12/11/19 Credit: 4 hours Section Title: Approx & Probabilistic Comp Section Info: Course Name: Approximate and Probabilistic Computing Across the Stack Course Abstract: The current drive for energy-efficiency has made approximation a key concept in designing and implementing software in various areas, such as data analytics, mobile computing, multimedia processing, and engineering simulations. This course will focus on foundations and system-level techniques for representing uncertainty in program's data and reasoning about profitable tradeoffs between accuracy, reliability, and energy consumption. In addition to selected algorithmic-level approximations, we will study (i) programming languages that natively operate on probabilistic and/or uncertain data, (ii) compilers that automatically approximate programs while verifying or testing the accuracy of optimized programs, and (iii) hardware devices that expose approximate components. The course will include lectures, reading research papers, in-class discussions, and a final research project. Restriction(s): Restricted to Graduate - Urbana-Champaign.
46042	Lecture-Discussion	SS	9:30AM -10:45AM	TR	Siebel Center for Comp Sci	Sinha, S	Part of Term: 1 Date Range: 08/26/19-12/11/19 Credit: 4 hours Section Title: Advance Bioinformatics Section Info: This course introduces a selection of topics in bioinformatics (mostly genomics) with a focus on probabilistic methods and statistical analysis, as well as basic principles of data science and computational sciences. Who this is for: The course will help graduate students aspiring to become bioinformatics researchers as well as students who are interested in data sciences in general and are looking for interesting applications. The course is less ideal for students interested in a casual exposure to the buzz surrounding bioinformatics. A research project (conceptualization and implementation) is a major component of the course grade, making the course unsuitable for students with little or no programming experience. Syllabus will tentatively include: Basic Molecular Biology, Probability/Statistics (probabilistic modeling, hypothesis testing, sampling), Introduction to Selected Bioinformatics topics (such as sequence alignment, enhancer prediction, epigenomics, modeling of gene expression, modeling of population evolution), and research paper reading on the selected topics. Restriction(s): Restricted to Graduate - Urbana-Champaign.
70199	Lecture-Discussion	TMC	11:00AM -12:15PM	WF	Siebel Center for Comp Sci	Chan, T	Part of Term: 1 Date Range: 08/26/19-12/11/19 Credit: 4 hours Section Title: Geometric Data Structures Section Info: This course is about data structures in computational geometry -- how to store large amounts of geometric data to support fast querying. Many such problems can be viewed as generalizations of standard one-dimensional searching. We will discuss fundamental techniques and recent theoretical developments for basic problems such as point location, range searching, dynamic convex hulls, and (low- and high-dimensional) approximate nearest neighbors. We will also explore geometric data structures in different settings, from the word RAM model to computational models inspired by big data, including external memory and streaming. Prerequisite: CS 374 or equivalent. Restriction(s): Restricted to Graduate - Urbana-Champaign.