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fall 2018 courses

C S 380S Theory & Prac of Secure Systems

Prof. H. Shacham, TTh 9:30-11 am. Survey of modern security, designed to introduce the basic techniques used in the design and analysis of secure systems.

C S 383C Numerical Anly: Linear Algebra

Prof. G. Biros, TTH 9:30-11am. Survey of numerical methods in linear algebra: floating-point computation, solution of linear equations, least squares problems, algebraic eigenvalue problems.

C S 384G Computer Graphics

Prof., D. Fussell, TTh 12:30-2 pm. Advanced material in computer graphics, including in-depth treatments of techniques for realistic image synthesis, advanced geometric modeling methods, animation and dynamic simulation, scientific visualization, and high-performance graphics architectures.

C S 386C Dependable Computing Systems

Prof. A. Mok, TTh 2-3:30pm. System models from synchronous to asynchronous, with emphasis on in-between models such as the timed asynchronous model. Control structures such as timed state-transition systems, and constraints in temporal and real-time logics. Analysis techniques such as model checking of timed systems, and extended Presburger arithmetic. Basic building blocks such as clock synchronization, synchronous atomic broadcast, time-bounded membership protocols, real-time scheduling theory, and state recovery methods. Practical implementation issues such as special operating system data structures and algorithms, open system design, and security concerns.

C S 386L Programming Languages

Prof. W. Cook, TTh 2-3:30 pm. Topics include formal syntax representations, program correctness, typing, and data abstraction. Features and problems in languages that allow parallelism. Exploration of different programming styles, such as imperative, functional, logic, data flow, and object-oriented programming.

C S 386W Wireless Networking

Prof. L. Qiu, M 1-4pm. Fundamental concepts and principles of wireless network technologies and protocol design, ranging from physical layer to application layer, and in-depth studies of current wireless research.

C S 388 Natural Language Processing

Prof. G. Durrett, TTh 9:30-11 am. Computational methods for syntactic and semantic analysis of structures representing meanings of natural language; study of current natural language processing systems; methods for computing outlines and discourse structures of descriptive text.

 

C S 388G Algorithms: Techniques and Theory

Prof. V. Ramachandran, TTh 2-3:30 pm. Sorting and searching algorithms, graph algorithms, algorithm design techniques, lower bound theory, fast Fourier transforms, NP-completeness.

C S 393R Autonomous Robots

Prof. P. Stone, TTh 9:30-11 am. Covers the steps necessary to create and program fully functional teams of autonomous robots, including locomotion, object manipulation, vision (segmentation and object detection), localization, inter-robot communication, Kalman filters and control theory, individual behavior creation, and multiagent coordination and strategic reasoning.

 

C S 394N Neural Networks

Prof. R. Miikkulainen, W 9am-12:00pm. Biological information processing; architectures and algorithms for supervised learning, self-organization, reinforcement learning, and neuro-evolution; theoretical analysis; hardware implementations and simulators; applications in engineering, artificial intelligence, and cognitive science.

C S 395T Approximability

Prof. D. Moshkovtiz. MW 2-3:30 pm. This class is about approximation algorithms and their limitations. It covers: combinatorial approximation algorithms; approximation algorithms based on linear and semidefinite programming; hierarchies of linear and semidefinite programming; limitations of the aforementioned techniques with connections to communication complexity and proof complexity; hardness of approximation and connections to multi-prover games and probabilistic checking of proofs; the proof of the PCP theorem, including combinatorial and algebraic techniques; sum-check; linearity testing; low degree testing; locally testable and decodable codes; composition; parallel repetition; long code, Fourier analysis and optimal inapproximability results; the Unique Games Conjecture; dictator tests and integrality gaps.

C S 395T Datacenters

Prof. S. Peter, TTh 11-12:30 pm. This course covers a set of advanced topics in data centers. The focus is on principles, architectures, and protocols used in modern data centers. We will cover hardware architectures, networks, operating systems, run-time systems, and applications. The goal of the course is to build on basic computer architecture, networking and operating systems course material to provide an understanding of large, complex networked systems, and provide concrete experience of the challenges through a set of labs.

C S 395T Deep Learning Seminar

Prof. P. Kraehenbuehl, MW 2-3:30 pm. This class covers advanced topics in deep learning, ranging from optimization to computer vision, computer graphics and unsupervised feature learning, and touches on deep language models, as well as deep learning for games. This is meant to be a very interactive class for upper level students (MS or PhD). For every class we read two recent research papers (most no older than two years), which we will discuss in class.

C S 395T Concepts of Info Retrieval

Prof. M. Lease, TTh 3-6 pm. 

In an Information Age promising instant access to seemingly limitless digital information, search has become the dominant paradigm for enabling information access. Creating an effective search engine, however, requires addressing many important challenges:

    Characterizing the nature of search relevance (both topical and user-oriented)

    Defining operational models for ranking based on relevance

    Developing search algorithms which are accurate, scalable, and efficient

    Designing interfaces, interaction mechanisms, and graphical visualizations providing an engaging and user-friendly search experience (human-computer interaction and visualization)

    Understanding trade-offs between system-oriented and user-oriented methods for search evaluation 

Information Retrieval (IR) studies both human information needs and the systems built to meet those needs. As such, IR has lain squarely at the intersection of Information Science and Computer Science since its inception. IR studies methods for capturing, representing, storing, organizing, and retrieving unstructured or loosely structured digital information, as well as designing interface, interaction, and visualization methods for creating an effective and compelling search experience. While digital information was once restricted to electronic documents, today's landscape of digital content is incredibly rich and diverse, including Web pages, news articles, books, transcribed speech, email, blogs (and micro-blogs), images, and video. The rise of the Web as a massive, global repository and distribution network has earned Web search engines and other Web technologies particular importance in organizing and finding information today. 

C S 395T Intro to Cognitive Science

Prof. D. Beaver, TTh 3:30-5 pm. A graduate level introduction to the study of cognition, computation, mind and brain from an interdisciplinary perspective. We will study contemporary views of how the mind works, the nature of reason, and how mental processes are reflected in behavior. Central to the course will be the modern computational theory of mind. We will study the embodiment of that theory in a few pounds of grey meat, what we know about how that lump of meat processes language, thinks thoughts, and develops consciousness, and the relationships between brains and modern computational architectures and between human intelligence and artificial intelligence. 

C S 395T Number Optimiz: Graphics/AI

Prof. Q. Huang, MW 3:30-5 pm. 

This course will cover a wide range of topics in numerical optimization. The major goal is to learn a set of tools that will be useful for research in Artificial Intelligence and Computer Graphics. A partial list of applications to be covered:

Iterative closest point method for rigid and non-rigid registration.

Robust image and shape denoising/smoothing.

Scalable geometry reconstruction.

Convex relaxations for MAP inference.

Stochastic gradient descent for optimizing neural networks.

Convex and non-convex optimizations for low-rank matrix recovery.

Policy gradient methods. 

C S 395T Topics in Learning Theory

Prof. A. Klivans, TTh 2-3:30 pm. This course focuses on fundamental theoretical problems from the field of machine learning.

 

 

C S 395T Verification and Synthesis for Cyberphysical Systems

Prof. U. Topcu, MW 3-4:30 pm. Autonomous robots and vehicles, smart medical devices and transplants, and advanced manufacturing platforms are only a few of the emerging systems that have been enabled by the advances in sensor, computation, communication, and control technologies. Systems that leverage the co-existence of these technologies are often called "cyberphysical systems". Over the last two decades, the growth in our ability to build cyberphysical systems has outpaced the growth in our ability to systematically specify, model and design them. This mismatch is now widely accepted a bottleneck in the affordable development of trustworthy cyberpyhsical systems. It also has motivated extensive recent research for creating methods and tools for the formal specification and automated verification and synthesis of cyberphysical systems.

This course provides an exposition to cyberphysical systems, theory and methods for developing these systems, and their applications in a range of domains, including autonomy, human-machine interaction, robotics, and networked systems. It primarily distills relevant principles from controls and formal methods, and highlights connections with optimization, networking, and learning.

 

 

spring 2017 courses

CS 380D Distributed Computing

Prof. Velayudhan, TTh 2-3:30pm. Models of distributed systems; language issues, proving properties of distributed systems; time, clocks, partial ordering of events; deadlock and termination detection; diffusing computations; computing in hostile environments; distributed resource management. Three lecture hours a week for one semester. Prerequisite: Graduate standing and Computer Science 372.

CS 384G Computer Graphics

Prof. Vouga, TTh 3:30-5pm. Same as Computational Science, Engineering, and Mathematics 382G. Advanced material in computer graphics, including in-depth treatments of techniques for realistic image synthesis, advanced geometric modeling methods, animation and dynamic simulation, scientific visualization, and high-performance graphics architectures. Three lecture hours a week for one semester. Computational Science, Engineering, and Mathematics 382G and Computer Science 384G may not both be counted. Prerequisite: Graduate standing; and Computer Science 354 or another introductory course in computer graphics, or equivalent background and consent of instructor.

CS 386D Database Systems

Prof. MIranker, M 9am-12pm. Introduction to the principles of database systems, including fundamental ideas and algorithms used in the construction of centralized database management systems, distributed database management systems, and database machines and their roles in Internet infrastructure. Topics include data storage and indexing algorithms, query processing and optimization, concurrency control, recovery, XML and object-oriented databases, database evaluation and tuning, and recent directions in database research. Three lecture hours a week for one semester. Prerequisite: Graduate standing and Computer Science 347 and 375.

CS 386L Programming Languages

Prof. Cook, TTh 9:30-11am. Topics include formal syntax representations, program correctness, typing, and data abstraction. Features and problems in languages that allow parallelism. Exploration of different programming styles, such as imperative, functional, logic, data flow, and object-oriented programming. Three lecture hours a week for one semester. Prerequisite: Graduate standing, and Computer Science 345 or consent of instructor.

CS 388 Natural Language Processing

Prof. Mooney, MW 2:00-3:30pm. Computational methods for syntactic and semantic analysis of structures representing meanings of natural language; study of current natural language processing systems; methods for computing outlines and discourse structures of descriptive text. Three lecture hours a week for one semester. Prerequisite: Graduate standing, and a course in artificial intelligence or consent of instructor.

CS 388C Combinatorics and Graph Theory

Prof. Gal, TTh 12:30-2pm. Counting, matching theory, extremal set theory, Ramsey theory, probabilistic method, linear algebra method, coding theory. Applications to computer science, including randomized algorithms. Three lecture hours a week for one semester. Prerequisite: Graduate standing, and Computer Science 336 or the equivalent or consent of instructor. An understanding of elementary proof and counting techniques is assumed.

CS 388G Algorithms: Techniques and Theory

Prof. Plaxton, MW 2-3:30pm. Sorting and searching algorithms, graph algorithms, algorithm design techniques, lower bound theory, fast Fourier transforms, NP-completeness. Three lecture hours a week for one semester. Prerequisite: Graduate standing, and Computer Science 357 or the equivalent or consent of instructor.

CS 388L Introducation to Mathematical Logic

Prof. Lifschitz, TTh 12:30-2pm. Introduction to some of the principal topics of mathematical logic: propositional and predicate calculus; Goedel's completeness theorem; first-order theories; formalizing mathematical reasoning; first-order arithmetic; recursive functions; Goedel's incompleteness theorems; axiomatic set theory. Three lecture hours a week for one semester. Prerequisite: Graduate standing and experience in abstract mathematical thinking.

CS 389L Automated Logical Reasoning

Prof. Dillig, TTh 3:30-5pm. Subjects include automated reasoning techniques for propositional logic, first-order logic, linear arithmetic over reals and integers, theory of uninterpreted functions, and combinations of these theories. Examines automated logical reasoning both from a theoretical and practical perspective, giving a hands-on experience building useful tools, such as SAT and SMT solvers. Three lecture hours a week for one semester. Computer Science 389L and 395T (Topic: Automated Logical Reasoning) may not both be counted. Prerequisite: Graduate standing.

CS 391D Data Mining: A Mathematical Perspective

Prof. T. Dhillon, F 9am-12pm. Mathematical and statistical aspects of data mining. Topics include supervised learning (regression, classification, support vector machines) and unsupervised learning (clustering, principal components analysis, dimensionality reduction). Uses technical tools that draw from linear algebra, multivariate statistics, and optimization. Three lecture hours a week for one semester. Computer Science 391D and 395T (Topic: Data Mining: A Statistical Learning Perspective) may not both be counted. Prerequisite: Graduate standing, and Mathematics 341 or the equivalent.

CS 392F Automated Software Design

Prof. Batory, TTh 2:00-3:30pm. Model driven engineering; metamodels, UML diagrams, constraints, transformations, software product lines, feature models, feature modularity, category theory, functors, commuting diagrams, program algebras, feature interactions, multi-dimensional separation of concerns, design-by-transformation, parallel software architectures, correct-by-construction, architecture refinement, optimization, and extension, program refactorings, design patterns, refactoring scripts. Three lecture hours a week for one semester. Computer Science 392F and 395T (Topic: Feature-Oriented Programming) may not both be counted. Prerequisite: Graduate standing, and a basic knowledge of Java, compilers and grammars, and object-oriented design methods.

CS 395T Computational Statistics with Application to Bioinformatics

Prof. Bajaj, MW 11:00am-12:30pm. This course dwells on fundamentals and algorithms for data in high dimensions with provably probabilistic bounds on the accuracy. The emphasis shall be on high dimensional linear and non-linear geometric data with applications in Bioinformatics and Scientific Computing. The course is aimed at first or second year graduate students, especially in the CSEM, CS, and ECE programs, but others are welcome. 

CS 395T Hardware Verification

Prof. Hunt, MW 3:30-5pm.

 

CS 395T Physical Simulation

Prof. Vouga, TTh 2-3:30pm. This project-oriented course will introduce you to the key concepts and algorithms for simulating physical systems: starting from the ground up with particle systems and mass-spring networks, we will move on to cover topics such as rigid and elastic bodies, collisions, cloth, and fluids.

 

CS 395T Prediction Mechanisms in Computer Architecture

Prof. Lin, F 10am-12pm. This research-focused course will explore uses of prediction to improve hardware performance. The goal is to understand the state-of-the art, to understand basic concepts and mechanisms for performing prediction, including machine learning, and to identify new ideas for advancing the state-of-the art. Students will focus on caching and data prefetching, but may also pursue other topics in computer architecture if they are interested.

 

CS 396M Advanced Networking Protocols

Prof. Lam, TTh 4-5:30pm. Topics include routing, multiple access, internetworking, security, performance models, and verification methods. Three lecture hours a week for one semester. Prerequisite: Graduate standing.