Computational Aspects of VLSI (Principles of Computer Science Series)

CS Courses
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CS Courses | EECS at UC Berkeley

Offered: jointly with LING CSE Introduction to Artificial Intelligence 3 Principal ideas and developments in artificial intelligence: Problem solving and search, game playing, knowledge representation and reasoning, uncertainty, machine learning, natural language processing. CSE Introduction to Embedded Systems 4 Introduces the specification, design, development, and test of real time embedded system software. Use of a modern embedded microcomputer or microcontroller as a target environment for a series of laboratory projects and a comprehensive final project.

Prototype a substantial project mixing hardware, software, and communications. Focuses on embedded processors, programmable logic devices, and emerging platforms for the development of digital systems.

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Provides a comprehensive experience in specification, design, and management of contemporary embedded systems. CSE Autonomous Robotics 4 Theory and application of algorithms and probabilistic techniques for autonomous robotics.

Course emphasizes the development process, rather than the product. CSE Computer Security 4 Foundations of modern computer security, including software security, operating system security, network security, applied cryptography, human factors, authentication, anonymity, and web security. CSE Introduction to Synthetic Biology 3 Studies mathematical modeling of transcription, translation, regulation, and metabolism in cell; computer aided design methods for synthetic biology; implementation of information processing, Boolean logic and feedback control laws with genetic regulatory networks; modularity, impedance matching and isolation in biochemical circuits; and parameter estimation methods.

Kueh Covers advanced concepts in system and synthetic biology.

ISBN 10: 0914894951

Includes kinetics, modeling, stoichiometry, control theory, metabolic systems, signaling, and motifs. All topics are set against problems in synthetic biology. CSE Laboratory Methods in Synthetic Biology 4 Designs and builds transgenic bacterial using promoters and genes taken from a variety of organisms.

Uses construction techniques including recombination, gene synthesis, and gene extraction. Evaluates designs using sequencing, fluorescence assays, enzyme activity assays, and single cell studies using time-lapse microscopy. Topic selection will vary from quarter to quarter and may include data privacy and security, data anonymization, hypothesis-testing on a shared database, impact of data science-based decisions on society.

Includes both guest speakers and case-study or article-based discussions. CSE Undergraduate Seminar , max. Topic selection will vary from quarter to quarter. CSE Project Practicum []-, max.

ISBN 13: 9780914894957

Projects may involve a group of students. CSE Undergraduate Research Seminar 1 Students prepare and give a public talk on their faculty-sponsored research projects.

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CSE Senior Project []-, max. Objectives: 1 integrating material from several courses, 2 introducing the professional literature, 3 gaining experience in writing a technical document, and 4 showing evidence of independent work. CSE Reading and Research , max. Free elective, but does not replace core course or computer science elective. CSE Programming Language Analysis and Implementation 4 Design and implementation of compilers and run-time systems for imperative, object-oriented, and functional languages.

Intra- and interprocedural analyses and optimizations. CSE Software Engineering 4 Specification, implementation, and testing of large, multiperson, software systems. Topics include abstraction, information hiding, software development environments, and formal specifications. CSE Advanced Topics in Software Engineering 4 Topics vary but may include software design and evolution, formal methods, requirements specifications, software and system safety, reverse engineering, real-time software, metrics and measurement, programming environments, and verification and validation.

CSE Principles of Programming Languages 4 Design and formal semantics of modern programming languages, includes functional and object-oriented languages. CSE Advanced Topics in Programming Languages 4 May include functional, object-oriented, parallel, and logic programming languages; semantics for languages of these kinds; type declaration, inference, and checking including polymorphic types ; implementation issues, such as compilation, lazy evaluation, combinators, parallelism, various optimization techniques.

Implementation project required. CSE Computer-Aided Reasoning for Software 4 Covers theory, implementation, and applications of automated reasoning techniques, such as satisfiability solving, theorem proving, model checking, and abstract interpretation. Topics include concepts from mathematical logic and applications of automated reasoning to the design, construction, and analysis of software.

CSE Advanced Topics in Human-Computer Interaction 4 Content varies, including interface issues for networks, embedded systems, education applications, safety and critical systems, graphics and virtual reality, databases, and computer-supported cooperative work. CSE Data Visualization 4 Covers techniques and algorithms for creating effective visualizations based on principles from graphic design, visual art, perceptual psychology, and cognitive science.

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Topics include data and image models; visual encoding; graphical perception; color; animation; interaction techniques; graph layout; and automated design. Lectures, reading, and project. CSE Statistical Methods in Computer Science 4 Introduction to the probabilistic and statistical techniques used in modern computer systems.

Graphical models, probabilistic inference, statistical learning, sequential models, decision theory. Topics include language models, text, classification, tagging, parsing, machine translation, semantic, and discourse analysis. Only computer science graduate students may register, although others are encouraged to attend.

CSE Design and Analysis of Algorithms I 4 Principles of design of efficient algorithms: recursion, divide and conquer, balancing, dynamic programming, greedy method, network flow, linear programming. Correctness and analysis of algorithms. Prerequisite: either CSE or equivalent. Content varies and may include such topics as algebraic algorithms, combinational algorithms, techniques for proving lower bounds on complexity, and algorithms for special computing devices such as networks or formulas.

Geometric computation, range searching, convex hulls, proximity, Vornoi diagrams, intersection.

Computational Aspects of VLSI (Principles of Computer Science Series)

All types of biomaterials as well as their applications in biomedical fields are introduced and discussed extensively. Transportation and Supply Chain Systems TTE 3 credits A study of engineering decision problems for transportation and supply chain systems, relying primarily on the quantitative methods of operations research. Catalog Description: Development of Computer Science topics appearing in Foundations of Data Science C8 ; expands computational concepts and techniques of abstraction. Common optimization targets for system-on-chip designs follow, with explanations of each. These are the questions we will investigate in this course.

Application areas include VLSI design and computer graphics. CSE Parallel Algorithms 4 Design and analysis of parallel algorithms: fundamental parallel algorithms for sorting, arithmetic, matrix and graph problems, and additional selected topics. Emphasis on general techniques and approaches used for developing fast and efficient parallel algorithms and on limitations to their efficacy. CSE Randomized Algorithms and Probabilistic Analysis 4 Examines algorithmic techniques: random selection, random sampling, backwards analysis, algebraic methods, Monte Carlo methods, and randomized rounding; random graphs; the probabilistic method; Markov chains and random walks; and analysis tools: random variables, moments and deviations, Chernoff bounds, martingales, and balls in bins.

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CSE Cryptography 4 Introduction to the theoretical foundation of cryptography, teaching the design and application of selected important cryptographic objects, and the mathematical frameworks and methodologies of modern cryptography for formalizing security goals and developing provably secure solutions. CSE Computational Biology 4 Introduces computational methods for understanding biological systems at the molecular level.

Problem areas such as mapping and sequencing, sequence analysis, structure prediction, phylogenic inference, regulatory analysis. Techniques such as dynamic programming, Markov models, expectation-maximization, local search. CSE Computational Neuroscience 3 Introduction to computational methods for understanding nervous systems and the principles governing their operation. Topics include representation of information by spiking neurons, information processing in neural circuits, and algorithms for adaptation and learning.

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Prerequisite: elementary calculus, linear algebra, and statistics, or permission of instructor. CSE Neural Control of Movement: A Computational Perspective 3 Systematic overview of sensorimotor function on multiple levels of analysis, with emphasis on the phenomenology amenable to computational modeling. Topics include musculoskeletal mechanics, neural networks, optimal control and Bayesian inference, learning and adaptation, internal models, and neural coding and decoding. CSE Computational Complexity I 4 Deterministic and nondeterministic time and space complexity, complexity classes, and complete problems.

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Time and space hierarchies. Alternation and the polynomial-time hierarchy.

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Circuit complexity. Probabilistic computation. Exponential complexity lower bounds. Interactive proofs. CSE Computational Complexity II 4 Advanced computational complexity including several of the following: circuit complexity lower bounds, p and counting classes, probabilistically-checkable proofs, de-randomization, logical characteristics of complexity, communication complexity, time-space tradeoffs, complexity of data structures.

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Relational databases, enforcement of integrity constraints. Object-oriented databases and object-relational databases.