fall 2016 courses

C S 380C Compilers

Prof. Pingali, TTH 11am-12:30pm. This course will focus on advanced techniques for program optimization and verification, with a focus on multicore processors and concurrency.  We will begin with classical topics such as interprocedural and intraprocedural dataflow analysis, abstract interpretation, and optimization techniques for modern uniprocessors. Then we will discuss multicore processors, and study techniques such as dependence analysis, loop transformations, points-to analysis, and shape analysis for optimizing the execution of regular and irregular programs on multicore processors. The last part of the course focuses on automatic program verification techniques.

C S 381V Visual Recognition

Prof. Kristen Grauman, W 1-4:00pm. This is a graduate seminar course in computer vision. We will survey and discuss current vision papers relating to visual recognition (primarily of objects and object categories), autoannotation of images, and scene understanding. The goals of the course will be to understand current approaches to some important problems, to actively analyze their strengths and weaknesses, and to identify interesting open questions and possible directions for future research.

C S 383C Numerical Anly: Linear Algebra

Prof. G. Biros, TTH 9:30-11am. Matrix Computations arise in a varied number of applications, such as, quantum chemistry computations, statistics, economics, data mining, etc. This first year graduate course focuses on some of the fundamental computations that occur in these applications. The standard problems whose numerical solutions we will study are (i) systems of linear equations, (ii) least squares problems, (iii) eigenvalue problems as well as SVD computations. We will also learn basic principles applicable to a variety of numerical  problems and apply them to the standard problems. These principles include (i) matrix factorizations, (ii) perturbation theory and condition numbers, (iii) effects of roundoff error on algorithms and (iv) analysis of the speed of algorithms.

C S 386C Dependable Computing Systems

Prof. A. Mok, TTH 2-3:30pm.

C S 386L Programming Languages

Prof. W. Cook, TTH 9:30-11am. This course is an in-depth investigation of the theory of programming languages. The course covers the fundamental tools used in the analysis and design of programming languages, including semantics, type theory, abstract interpretation, metaprogramming, and partial evaluation. We will also consider their application to imperative, functional, and object-oriented languages.

C S 386W Wireless Networking

Prof. L. Qiu, M 10am-1pm.

C S 388H Cryptography

Prof. B. Waters, MW 9-10:30am. The objective of this course is to familiarize the students with a foundational background in cryptography. Topics will include foundations, public key cryptography, secure formalization, symmetric key cryptography and zero knowledge proofs. Key components of this course are understanding how to precisely formulate security definitions and how to rigoursly prove theorems. This course is designed to be a challenging theory course.

C S 388P Parallel Algorithms

Prof. V. Ramachandran, TTH 2-3:30pm. Introduction to parallel algorithms (Time/work optimality; some basic result), Introduction to parallel models (Distributed memory: networks; routing; BSP, Shared memory: caching; multicore), Efficient PRAM computations (Prefix sums; merging; merge-sort; bitonic sort; list ranking; tree computations; graph connectivity; ear decomposition; lower bounds), Parallel complexity (RNC, maximum matching; NC, Boolean circuit families; logspace, space complexity, parallel computation thesis), P-completeness, Network, BSP, and multicore algorithms (Randomized routing on hypercube; EREW emulation on BSP; multicore scheduling).

C S 388S Formal Semantics & Verification

Prof. E. Emerson, TTH 6:30-8pm. Topics: Review of discrete math and automata theory, Floyd's method for flowchart programs, Hoare's compositional logic for structured programs, Dijkstra's weakest preconditions for nondeterministic programs, Pnueli's temporal logic for nonterminating concurrent programs, Clarke, Emerson, Sifakis's model checking for finite state concurrent program

C S 393R Autonomous Robots

Prof. P. Stone, TTH 9:30-11am. This class is a graduate introduction to autonomous robotics and serves as a core class in the GraduatePortfolio Program in Robotics. There will be some assigned readings and discussions pertaining to robotics and multi-robot systems in general. Students will learn how to program the components of a team of robots
for playing soccer under the rules of the RoboCup standard platform league using Aldebaran Nao robots. All students will learn how to program the robots directly. Challenges to be addressed will include some or all of the following:
Control theory, Observers and tracking, Localization, Vision (segmentation and object detection), Behavior, Applications, Social Implications

C S 394N Neural Networks

Prof. R. Miikkulainen, TH 9am-12:00pm. The main goals of the class are to (1) obtain an overview of current state of the art in the field, (2) carry out a substantial research project, and (3) get practice in research skills such as conducting a literature study, putting together a research and a conference talk, and writing a research paper. The course is organized so that selecting and completing a research project should be as easy as possible. The first part of the course is an introduction to neural networks. Biological information processing is first briefly discussed, followed by an overview of the most important artificial neural network architectures and algorithms such as backpropagation, self-organizing maps, reinforcement learning, and neuroevolution. Distributed representations will be introduced and the foundations of connectionist artificial intelligence will be discussed. The second part is research oriented. Each team of 2 students will select an advanced topic in neural networks, study the literature in depth and give a 50 min presentation to the class on that topic.

C S 395T Concepts of Information Retrieval

Prof. M. Lease, TH 12:00-3:00pm. 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. http://courses.ischool.utexas.edu/Lease_Matt/2016/Fall/CS395T/

C S 395T Information Policy

Dr. A. Newell, M 2-5:00pm.

Internet & Information Policy is a survey course relevant for students interested in understanding the varied components of Information Policy as related to the Internet and engaging in in-depth study or practical experience with an aspect of Information Policy of particular interest to the individual student. Topics covered in the course include Information Economics; Bitcoin and Internet Currencies; Data, Information, and Open Data and Applications in Planning and Policy Making; Interoperability; The Dark Web; Information Security; Cybersecurity and Global Internet Governance; Hacktivism and Social Media; The Information Organization and Complex Adaptive Systems Theory; Planning and Policy Applications for the Future of Information and the Internet of Things; and The Singularity; among others.

Students will engage in readings, blogging, practical experiences, and guest lectures to cover the subject matter and will be responsible for completing an individual or group project related to a topic of their choosing within the purview of Information Policy. Examples of student projects include engaging in Bitcoin exchange and documenting the experience to discern any policy applications, exploring the Dark Web for any potential uses for public good, conducting field research to understand how user design plays a role in open data and local policy making, researching cybersecurity policies and enhancements or hindrances to economic growth, designing a drone program to assist in food provision, designing an app for disease mitigation, discovering the regulatory barriers to financial inclusion in mobile banking, understanding and exploring any role 3D printing might play in affecting health and health informatics, connecting businesses and local organizations to open data to spur innovation, and those yet to be unleashed by the student imagination.

C S 395T Intro to Cognitive Science

Prof. D. Beaver, T 3:30-6:30pm. This course is an undergraduate introduction to the study of intelligence, mind and brain from an interdisciplinary perspective. We will study contemporary views of how the mind works, the nature of reason, and how thought processes are reflected in the language we use. Central to the course will be the modern computational theory of mind, the embodiment of that theory in a few pounds of grey meat, and what we know about how that lump of meat processes language, thinks thoughts, and develops consciousness. These topics are central to the inter-disciplinary field of Cognitive Science, an area in which UT boasts enormous strength. The course will be led by the current director of Cognitive Science at UT, and incorporate presentations from faculty in Computer Science, Linguistics, Neuroscience, Philosophy, and Psychology.

C S 395T Physical Simulation & Animation for Computer Graphics

Prof. P. Vouga, TTH 12:30-2:00pm. An increasingly important sub-area of computer graphics is physics-based simulation: such simulations are used by movie studios for creating realistic special effects, game engines like Bullet and ODE, interactive design tools for architecture and 3D printing, tools for studying problems in biology and soft-matter physics, etc. 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.

C S 395T Robot Learning

Prof. S. Neikum, TTH 11am-12:3pm. This course will survey a variety of machine learning techiques that allow robots to learn from human demonstrations, interactions, and experiences in the world. Rather than exclusively focus on techniques that directly involve humans, we will also discuss autonomous learning methods that can reduce the burden of robot programmers and end-users. Topics will include motion generalization, supervised learning, reinforcement learning, inverse reinforcement learning, feature selection, skill acqusition, active learning, natural language processing, human-robot collaboration, and human factors.

C S 395T Sublinear Algorithms

Prof. E. Price, TTH 3:30-5pm. This graduate course will study algorithms that can process very large data sets. In particular, we will consider algorithms for: Data streams, where you don't have enough space to store all the data being generated. Property testing, where you don't have enough time to look at all the data. Compressed sensing, where you don't have enough measurement capacity to observe all the data.

C S 395T Verification and Synthesis for Cyberphysical Systems

Prof. U. Topcu, TTH 2-3:30pm. Topics: Modeling & finite transition systems, linear time propertires, automata based representations, temporal logic, model checking, closed-system snthesis & demo, probablistic varification (DTMCs and MDPs), open systems synthesis, hybrid systems, abstract based control, optimization primer, deductive verification

C S 398T Supervised Teaching in Computer Science

Dr. A. Norman. The class meets for the first 10-12 weeks of the semester, and the students participate in independent study for the rest of the semester. For approximately the first half of the course, there will be research talks by several CS faculty members. For the rest of the course, the course will cover topics related to teaching and TA training.


spring 2017 courses

CS 391L Machine Learning

Prof. Ballard, MW 9:30-11am. Students will learn about sampling distribution, decision trees, Bayes nets, and Markov models.

CS 389L Automated Logical Reasoning

Prof. I. Dillig, TTh 11-12:30pm. In this course, we will study widely-used logical theories and decision procedures for answering whether formulas in these theories are satisfiable. In particular, we will consider automated reasoning techniques for propositional logic, first-order logic, linear arithmetic over reals and integers, theory of uninterpreted functions, and combinations of these theories. We will also look at applications of logic, particularly in program verification..

CS 384G Computer Graphics

Prof. Fussell, TTh 12:30-2pm. Students will learn about displays, sampling theory, image processing, Ggometric transformations, shading, ray tracing, texture mapping, Z-buffers, hierarchical modeling, parametric curves, procedural models, particle systems, subdivision curves, parametric surfaces, subdivision surfaces, perception and color, GPUs and Programmable Shaders.

CS 389R Recursion and Induction

Prof. Hunt, TTh 11-12:30pm. This course concerns itself with mathematically modeling computation and analyzing these models.

CS 386D Database Systems

Prof. Miranker, M 9am-12pm. Focus on the Semantic Web and data integration. One theme of term project ideas concerns leveraging relational database theory and architecture to improve the implementation of the Semantic Web.

CS 388 Natural Language Processing

Prof. Mooney, MW 2:00-3:30pm. The intent of the course is to present a fairly broad graduate-level introduction to Natural Language Processing (NLP, a.k.a. comptuational linguistics), the study of computing systems that can process, understand, or communicate in human language. The primary focus of the course will be on understanding various NLP tasks as listed on the course syllabus, algorithms for effectively solving these problems, and methods for evaluating their performance. There will be a focus on statistical learning algorithms that train on (annotated) text corpora to automatically acquire the knowledge needed to perform the task.

CS 388H Cryptography

Prof. Waters, MW 9:00-10:30am. The objective of this course is to familiarize the students with a foundational background in cryptography. Topics will include foundations, public key cryptography, secure formalization, symmetric key cryptography and zero knowledge proofs.

CS 388M Communication Complexity

Prof. Gal, TTh 12:30-2pm.

CS 395T Advanced Geometry Processing

TTh 9:30-11:00am.

CS 395T Physical Simulation and Animation for Computer Graphics

Prof. Vouga, TTh 12:30-2pm. 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 Auto Verification of Software

Prof. T. Dillig, M 1:00-4:00pm.

CS 395T Prediction Mechanisms Comp Arc

Prof. C. Lin, TTh 2:00-3:30pm.

CS 395T Topics in Computer Science

Prof. C. Rossbach, MWF 1:00-2:00pm.

CS 396M Advanced Networking Protocols

Prof. S. Lam, TTh 4:00-5:30pm.