#nParallel Programming 
#dComputer Science 233A is a graduate course in parallel programming that covers in more depth the topics of CS 133. Emphasis is on examining state of the art research and individual research projects
#iRajive Bagrodia
#t2008,winter

#nLanguage and Thought
#dIntroduction to Natural Language Processing (NLP) and Cognitive Models that support NLP; Emphasis is on both Semantics/Logic and Empirical/Statistical Approaches to Natural Language Text.
#iMichael G. Dyer
#t2008,spring 

#nAnimat-Based Modeling
#dAnimats are mobile/sensing animal/insect-like software agents embedded in simulated dynamic continuous environments. Emphasis is on modeling action selection and goal-oriented behavior via neurocontrollers and survival/adaptation of populations of animats via reinforcement learning and evolutionary programming. Animat-based tasks include: food foraging, mate finding, group predation, navigation, predator avoidance, cooperative nest construction, communication, and parenting. Course objective is to introduce students to issues that arise in modeling agents that must perform tasks while surviving and adapting to their environments. Issues include: smooth multi-modal sensory integration, motor coordination, learning from other agents (vicariously or via imitation), and achievement of sequences of high-level goals (e.g. for nest construction/repair) while still maintaining viability (e.g., eating when hungry; drinking when thirsty; avoiding predators, not getting lost, etc.). 
#iMichael G. Dyer
#t2008,Fall

#nAnimat-Based Modeling
#dAnimats are mobile/sensing animal/insect-like software agents embedded in simulated dynamic continuous environments. Emphasis is on modeling action selection and goal-oriented behavior via neurocontrollers and survival/adaptation of populations of animats via reinforcement learning and evolutionary programming. Animat-based tasks include: food foraging, mate finding, group predation, navigation, predator avoidance, cooperative nest construction, communication, and parenting. Course objective is to introduce students to issues that arise in modeling agents that must perform tasks while surviving and adapting to their environments. Issues include: smooth multi-modal sensory integration, motor coordination, learning from other agents (vicariously or via imitation), and achievement of sequences of high-level goals (e.g. for nest construction/repair) while still maintaining viability (e.g., eating when hungry; drinking when thirsty; avoiding predators, not getting lost, etc.). 
#iMichael G. Dyer
#t2009,Fall

#nArithmetic Processors and Algorithms 
#dHighlights. Levels of description. Number systems: conventional, redundant, and residue. Structure of algorithms and complexity measures.
#iMilos D. Ercegovac
#t2010,winter 

#nProtocol and Systems Design for Wireless Mobile Networks
#dThis graduate course provides an in-depth study of networking protocol and system software design in the area of wireless networking and mobile computing. It will help graduate students in the networking area establish a solid foundation in wireless networking protocols, fundamental concepts and principles, as well as network programming skills.  It will also introduce students to a few hot topics in wireless networking and mobile computing research.
#iSongwu Lu 
#t2009,fall

#nFormal Verification for Embedded Controllers
#dWe shall look at problems in the formal modeling, verification and validation of embedded real-time control systems. More and more computer systems interact with the physical world: from medical devices to automobiles to smart power grids. The design and analysis of these cyber-physical systems is difficult, as they must integrate the physical world with the discrete world of computation. This course will study formal techniques underlying the design and analysis of embedded computation. This is an active research area, and part of the course objective is to provide a coherent introduction to this emerging discipline. The first two-thirds of the class will consist of lectures covering the basic theory of cyber-physical systems. The last one-third will focus on recent research literature in the area. 
#iRupak Majumdar
#t2009,fall

#nSoftware Engineering
#dBuilding large software systems is hard. Building large systems that work is harder. Building working systems on time, within budget, and ahead of competitors, is harder still. This course covers techniques for dealing with the complexity of large software development projects. We will focus on technical aspects of software engineering for individuals and for small teams, rather than business or management aspects. We will cover requirements, specifications, principles of design, testing and debugging. 
#iRupak Majumdar
#t2009,spring

#nComplexity of Problems
#dThe field of computational complexity aims to classify problems according to the computational resources (time, space) necessary to solve them. This course is an introduction to complexity theory, with an emphasis on problems arising application areas such as automata theory, verification, and VLSI CAD. We shall study the complexity of some problems arising in these areas and use these problems to motivate the corresponding classes. We shall study relationships between complexity classes and also "canonical" problems for each class. Along the way, we shall study some extremely elegant and general techniques that have arisen through the study of complexity theory. Prerequisites for this class are an undergraduate algorithms course and preferably also an undergraduate theory of computation course (equivalent to CS 180 and CS 181 at UCLA). If you are unsure about your background, please talk to the instructor. I will not assume familiarity with various application areas, and will introduce them as needed. 
#iRupak Majumdar
#t2009,winter

#nFoundations of Cryptography; 
#dThis is a graduate course that introduces students to the theory of cryptography, stressing rigorous definitions and proofs of security. Topics include notions of hardness, one-way functions, hard-core bits, pseudo-random generators, pseudo-random functions and pseudo-random permutations, semantic security, public-key and private-key encryption, secret-sharing, message authentication, digital signatures, interactive proofs, zero-knowledge proofs, private information retrieval, collision-resistant hash functions, commitment protocols, key-agreement, contract signing and two-party secure computation with static security. 
#iRafail Ostrovsky
#twinter,2010

#nPattern Recognition and Machine Learning
#dThis course introduces fundamental concepts, theories, and algorithms for pattern recognition and machine learning,which are used in computer vision, speech recognition, data mining, statistics, information retrieval, and bioinformatics. Topics include: Bayesian decision theory, parametric and non-parametric learning, data clustering, component analysis, boosting techniques, kernel methods and support vector machine, and fast nearest neighbor indexing and
#iSong Chun Zhu
#t2009,fall

#nStatistical Modeling and Learning
#dThis graduate level course introduces the principles, theories, and algorithms for modeling complex patterns in very high dimensional spaces, learning these statistical models from large data sets, and verifying them through stochastic sampling (synthesis). More specifically we study two classes of statistical models.
#iSong Chun Zhu
#t2008,spring






