Letter grading. Letter grading. Letter grading. Project-based course to learn about best practices in health data collection and validation. (Same as Bioengineering CM286.)
Lecture, four hours; outside study, eight hours. Letter grading. Letter grading. Lecture, four hours; outside study, eight hours. Designed for engineering students as well as students from biological sciences and medical school. S/U or letter grading. Focus on achieving highest quality productions to qualify and submit products to Student Academy Awards competition. Topics include syntax and semantics of formal logic; algorithms for logical reasoning, including satisfiability and entailment; syntactic and semantic restrictions on knowledge bases; effect of these restrictions on expressiveness, compactness, and computational tractability; applications of automated reasoning to diagnosis, planning, design, formal verification, and reliability analysis. Designed for freshmen/sophomores. Theoretical foundations as well as practical design methods. Requisites: courses 111, 131. May be repeated for credit with topic change. May be repeated for credit.
Letter grading. Letter grading. Required preparation for graduate students: undergraduate-level knowledge of data structures and object-oriented program languages. Current research areas. Special topics in computer science for undergraduate students taught on experimental or temporary basis, such as those taught by resident and visiting faculty members. Lecture, four hours; outside study, eight hours. Bulk arrival and bulk service systems. (Same as Electrical and Computer Engineering M171L.) Laboratory, four hours; discussion, two hours; outside study, six hours. Introduction to interaction concepts, enabling students to create low-fidelity real-time three-dimensional animation and to concepts in artificial intelligence, enabling them to refine their interactions to create high-fidelity real-time three-dimensional animation.
Lecture, four hours; outside study, eight hours. Zoom Webinar Enforced requisite: course 174A. Lecture, three hours. In-depth treatment of systematic problem-solving search algorithms in artificial intelligence, including problem spaces, brute-force search, heuristic search, linear-space algorithms, real-time search, heuristic evaluation functions, two-player games, and constraint-satisfaction problems. Required: 12 units (at least 3 courses) of Science and Technology.
Students are divided into teams led by instructor; each team is assigned one external company or organization that they investigate as candidate for possible computerization, submitting team report of their findings and recommendations.
Requisite: course 131. Lecture, four hours; discussion, two hours; outside study, six hours. May be repeated for credit.
Lecture, four hours. Letter grading. Favorites, recommendations, and notifications are only available for UCLA Graduate Students at this time.
Letter grading. Information systems and database systems in enterprises. UCLA Computer Graphics & Vision Laboratory Courses. Lecture, four hours; outside study, eight hours. P/NP grading. Elliptic curve methods. Semistructured information.
Research techniques and experience on special topics involving models, modeling methods, and model/computing in biological and medical sciences. Lecture, three hours. Attacks on cryptosystems. In-depth studies of VLSI architectures and VLSI design tools. Lecture, four hours; outside study, eight hours. Tutorial, to be arranged. Diagonalization, polynomial-time hierarchy, PCP theorem, randomness and de-randomization, circuit complexity, attempts and limitations to proving P does not equal NP, average-case complexity, one-way functions, hardness amplification. Fundamental concepts, theories, and algorithms for pattern recognition and machine learning that are used in computer vision, image processing, speech recognition, data mining, statistics, and computational biology. Introduction to basic concepts of information security necessary for students to understand risks and mitigations associated with protection of systems and data. Requisite: course 233A. D. Doctor of Philosophy (Ph.D.) Visit the Program’s website. Letter grading. Memory management and protection, interrupts and traps, processes, interprocess communication, preemptive multitasking, file systems.
Background in discrete mathematics helpful. Limited to junior/senior USIE facilitators. Topics include (1) networking fundamentals: design philosophy of TCP/IP, end-to-end arguments, and protocol design principles, (2) networking protocols: 802.11 MAC standard, packet scheduling, mobile IP, ad hoc routing, and wireless TCP, (3) mobile computing systems software: middleware, file system, services, and applications, and (4) topical studies: energy-efficient design, security, location management, and quality of service. Both theory- and data-driven modeling, with focus on translating biomodeling goals and data into dynamical mathematical models, and implementing them for simulation, quantification, and analysis. Limited to juniors/seniors. Letter grading. Lecture, four hours; laboratory, four hours; outside study, four hours.
(Same as Statistics M241.) Letter grading. Flow and congestion control; bandwidth allocation. Letter grading. Laboratory, four hours; outside study, five hours. Enforced requisite: course 32. Formal techniques for verification of concurrent programs. (Same as Bioengineering CM187 and Computational and Systems Biology M187.) Letter grading. (Same as Bioengineering M296C and Medicine M270E.) Use of Unity Game Engine to make technical decisions to adapt stories to games. Requisite: course 181. UCLA Computer Science 101 Computer science is a branch of engineering that encompasses the design, modeling, analysis, and applications of computer systems. Letter grading. Requisite: course M51A or Electrical and Computer Engineering M16. Basics of numerical simulation algorithms, with modeling software exercises in class and PC laboratory assignments. Requisite: course 251A. Lecture, four hours; outside study, eight hours. Discussion of and critical thinking about topics of current intellectual importance, taught by faculty members in their areas of expertise and illuminating many paths of discovery at UCLA. Letter grading. Requisite: course M151B. Basic data types, operators and control structures. Computational techniques and methods include those from statistics and computer science. Examples show how to use these explicit models to gain clarity on nature of biosystem phenomena, and frame questions and explore new ideas for research. Machine learning allows computers to learn potentially complex patterns from data and to make decisions based on these patterns. Designed for graduate engineering students as well as students from biological sciences and medical school. On-chip and off-chip communication. Other topics at discretion of instructor.
Basics of numerical simulation algorithms, with modeling software exercises in class and PC laboratory assignments. Their... On Tuesday, October 13, 2020, the UCLA Computer Science Department held a private Virtual Career Fair through the software platform Gatherly. Individual contract with faculty mentor required. Emphasis on applications in simulation of physical systems. Winter 21. Requisite: course 180. (Same as Electrical and Computer Engineering M146.) Introduction to fundamental problem solving and knowledge representation paradigms of artificial intelligence. Computational aspects of processing visual and other sensory information. In-depth study of network protocol and systems software design in area of wireless and mobile Internet. Class hierarchy analysis, rapid type analysis, equality-based analysis, subset-based analysis, flow-insensitive and flow-sensitive analysis, context-insensitive and context-sensitive analysis. To browse courses by subject area, click on the subject name. Important concepts and theory for building effective and safe Web applications and first-hand experience with basic tools. Introduction to computational analysis of genetic variation and computational interdisciplinary research in genetics. Letter grading. Seminar, two hours; outside study, four hours. Requisite: Electrical Engineering 141 or 142 or Mathematics 115A or Mechanical and Aerospace Engineering 171A. Answer: All three majors are software degrees, but CS is purely software with no required hardware curriculum, CSE is software and little bit of hardware, CE is software and a little bit of hardware, but with a stronger emphasis on Electrical Engineering Design than CSE. S/U grading. (Same as Bioengineering CM186, Computational and Systems Biology M186, and Ecology and Evolutionary Biology M178.) May be repeated for credit with topic change. Letter grading. Computer Science Majors Only. Finite-state languages and finite-state automata. Letter grading. Designed for graduate students. How to properly generate and analyze health data. Concrete exploration of three major programming paradigms--functional, object-oriented, and logic programming--by prototyping implementations of languages in each.
Variable topics in computer science not covered in regular computer science courses. Teaches students how to use computers as tool for problem solving, creativity, and exploration through design and implementation of computer programs. File organization and secondary storage structures.
System-level management and cross-layer methods for power and energy consumption in computing and communication at various scales ranging across embedded, mobile, personal, enterprise, and data-center scale. CS 201: Representation Learning and Exploration in Reinforcement Learning, AKSHAY KRISHNAMURTHY, Microsoft Research – New York Trees, graphs, and associated algorithms.
Topics include notions of hardness, one-way functions, hard-core bits, pseudorandom generators, pseudorandom functions and pseudorandom permutations, semantic security, public-key and private-key encryption, secret-sharing, message authentication, digital signatures, interactive proofs, zero-knowledge proofs, collision-resistant hash functions, commitment protocols, key-agreement, contract signing, and two-party secure computation with static security.
Lecture, four hours; outside study, eight hours. Students practice communication skills with frequent assessment of and feedback on progress.
Letter grading. Lecture, four hours; laboratory, two hours; outside study, six hours. Introduction to machine learning.
Letter grading. Lecture, four hours; outside study, eight hours. S/U grading. Students work in teams to develop and implement designs and to document and give oral presentations of their work. Series-parallel stages. Preparation: C or C++ programming experience. Animat-based tasks include foraging, mate finding, predation, navigation, predator avoidance, cooperative nest construction, communication, and parenting.
Enforced requisite: course 180. Enforced corequisite: Honors Collegium 101E.
(Same as Chemistry CM160B.) Lecture, four hours; discussion, two hours; outside study, six hours. Tutorial, to be arranged. Designed for juniors/seniors. Fundamental design techniques that can be used to implement complex integrated systems on chips. Lecture, four hours; discussion, two hours; outside study, six hours.
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