The theory of quantum information and quantum computation. Introduction to Computer Programming. Applications of machine learning and statistics by using parallel computers to aggregate and analyze data streams from sensors. Detailed tutorials for synthesis and simulation tools using FPGAs and VHDL. Prerequisites: An advanced undergraduate course such as BIOL 421 or a graduate course in biology such as Biol 526 (Experimental Principles in Cell and Molecular Biology), BIOL 527 (Advanced Moleclar Genetics), BIOL 540 (Genetic Systems), or equivalent, is a prerequisite. Emphasis is placed on teaching methods and tools that are widely used in various areas of computer science. Computer vision technology has led to latest innovations in areas such as Hollywood movie production, medical diagnosis, biometrics, and digital library. Not offered 2020-21. Not offered 2020-21. Can the artistic, aesthetic, and scientific realms be bridged to effectively promote and interpret the past? The goal of this course is to provide an opportunity for seniors to define, desand execute a project of your own choosing that demonstrates the technical skiland abilities that you have acquired during your 4 years as undergraduates. CIS 191 Using and Understanding Unix and Linux. Topics include rate-code neural networks, their differential equations, and equivalent circuits; stochastic models and their energy functions; associative memory; supervised and unsupervised learning; development; spike-based computing; single-cell computation; error and noise tolerance. Sprawling cities, dense vegetation, infinite worlds - procedural graphics empower technical artists to quickly create complex digital assets that would otherwise be unfeasible. This course covers the basics of computer programming and provides a foundation for further learning in this area. One-time course offerings of special interest. It also provides guidelines for examining system training data for bias, representation (of race, gender and other characteristics) and ecological validity. Introduction to the theory, algorithms, and applications of automated learning. If time permits, we will also discuss biological example systems such as signal transduction, genetic regulatory networks, and the cytoskeleton. Students are required to submit a final written report and give a final presentation and demonstration of their project. The main goal of the course is to provide an introduction to the central concepts and core methods of statistical learning, an interdisciplinary field at the intersection of statistics, machine learning, information and data sciences. Prerequisites: Linear Algebra ACM/IDS 104 and Introduction to Probability Models ACM/EE/IDS 116 or their equivalents. 9 units (3-4-2): first term. Areas include DNA sequence alignment, genetic variation and analysis, motif discovery, study design for high-throughput sequencing RNA, and gene expression, single gene and whole-genome analysis, machine learning, and topics in systems biology. degree requirements. The answer to all these questions is in the affirmative provided we allow the use of randomization. By failing to prepare, you are preparing to fail. Prerequisites: basic knowledge of digital electronics. This course will cover core concepts in machine learning and statistical inference. Not offered 2020-21. Construction of VHDL libraries. Each year, the studio-seminar focuses on a project. For doctoral students studying a specific advanced subject area in computer and information science. This course is an introduction to quantum cryptography: how to use quantum effects, such as quantum entanglement and uncertainty, to implement cryptographic tasks with levels of security that are impossible to achieve classically. This is the second computer oganization course and focuses on computer hardware design. We will examine how XML standards enable information exchange; how web services support cross-platform interoperability (and what their limitations are); how to build high-performance application servers; how "cloud computing" services work; how to perform Akamai-like content distribution; and how to provide transaction support in distributed environments. List of Computer Science Course Offerings You are here. Topics include: discrete models, regression models, hierarchical models, model comparison, and MCMC methods. The students will be expected to use the Maple programming environment in homework exercises which will include: numerical and symbolic computations, simulations, and graphical displays. Counts as a Formal Reasoning course for College students. Undecidable problems: word problems for groups, solvability of Diophantine equations (Hilbert's 10th problem). In this course, algorithms will be implemented in Python on mobile platforms on ground and in the air. Students will have both written and practical, Python-based, assignments to build and deploy components of a blockchain solution, CIS 240 Introduction to Computer Systems. How can modern technologies be applied to do a better job at presenting what is difficult to experience firsthand? Basic power system analysis: phasor representation, 3-phase transmission system, transmission line models, transformer models, per-unit analysis, network matrix, power flow equations, power flow algorithms, optimal powerflow (OPF) problems, swing dynamics and stability. Prerequisites: Ma 2, Ma 3, Ma/CS 6a, CS 21, CS 38/138, and ACM/EE/IDS 116 or CMS/ACM/IDS 113 or equivalent. We will draw upon theory and practices from art, media, computer science and technology studies to critically analyze algorithms and their implementations within society. The goal of this course is to give students greater design and implementation experience in embedded software development and to teach them how to model, design, verify, and validate safety critical systems in a principled manner. Prerequisites: Ma 3 or ACM/EE/IDS 116 or CMS 117 or Ma/ACM/IDS 140a. This course will introduce supervised learning (decision trees, logistic regression, support vector machines, Bayesian methods, neural networks and deep learning), unsupervised learning (clustering, dimensionality reduction), and reinforcement learning. This course, when combined with EE 112, EE/Ma/CS/IDS 127, EE/CS 161, and EE/CS/IDS 167, should prepare the student for research in information theory, coding theory, wireless communications, and/or data compression. This course is to study emerging paradigms in IoT/Edge Computing and to learn how to develop data driven applications that can harness the power of the IoT/Edge computing. Undergraduates should register for CS 11. For Ph.D. candidates working exclusively on their dissertation research, having completed enrollment for a total of ten semesters (fall and spring). Programming will be required. This course is available for graduate students only. Prerequisite: Undergraduate-level knowledge of Operating Systems and Networking, programming experience. Prerequisite: College level introductory biology required; undergraduate or graduate level statistics taken previously or concurrently required; molecular biology and/or genetics encouraged; programming experience encouraged, Prerequisite: ((BIOL 101 AND BIOL 102) OR BIOL 121) AND STAT 111 AND STAT 112. But how do you create a software "product" as part of a team, with customers that have expectations of functionality and quality? The topics covered in the course will vary, but will be pulled from current research in the design, analysis, control, and optimization of networks. Study of programmable logic devices (CPLDs and FPGAs). This course is an introduction to the theory of Machine Learning, a field which attempts to provide algorithmic, complexity-theoretic and statistical foundations to modern machine learning. The lectures present a disciplined step-by-step approach to develop new ventures based on technological innovation in this space, and with invited speakers, cover topics such as market analysis, user/product interaction and design, core competency and competitive position, customer acquisition, business model design, unit economics and viability, and product planning. Supervised research or development in computer science by undergraduates. Graphs: paths, trees, circuits, breadth-first and depth-first searches, colorings, matchings. Hierarchical design and practice on the hard task of project splitting. Questions explored include: What do networks actually look like (and why do they all look the same)? Universal source codes. In this course, we'll explore how researchers and organizations like Microsoft, Google, and NASA are solving these hard problems, and we'll get to use some of the tools they've built! New topics are discussed each year. CIS 601 Advanced Topics in Computer Architecture. Can you be convinced of the correctness of an assertion without ever seeing the proof? The lectures will cover relevant background material at an advanced level and present results from selected recent papers within that year's chosen theme. Potential class projectinclude fly-throughs of architectural and landscape renderings, simulations of astronomy and cosmology, modeling of human behavior within architectural and landscape settings, and study artifacts in the Penn Museum. The online version of the Caltech Catalog is provided as a convenience; however, the printed version is the only The topics include finite automata and regular languages, context-free grammars and pushdown automata, Turing machines and undecidability, tractability and NP-completeness. movement control, path planning, decision making, etc. The course also will examine game development from an engineering point of view, including: game play mechanics, game engine software and hardware architectures, user interfaces, design documents, playtesting and production methods. The course divides into three major sections. Master's Degree Graduate Certificate Professional Program Graduate Credit CEU CME Topic. CSE 120: Computer Science Principles Introduces fundamental concepts of computer science and computational thinking. Not all programming languages are inherently procedural or object-oriented. The use of Graphics Processing Units for computer graphics rendering is well known, but their power for general parallel computation is only recently being explored. This course covers generations of wireless mobile network standards and systems, basic differences and their evolution, charting the development of mobile telecommunications systems from 3G, to today's state-of-the-art wireless technology 4G LTE, and the next generation wireless technology, 5G. Assignments will include problems drawn from fields such as graphics, numerics, networking, and games. Design and Construction of Programmable Molecular Systems. The Independen t Study may involve coursework, presentations, and formally gradable work comparable to that in a CIS 500 or 600 level course. Google translate can instantly translate between any pair of over fifty human languages (for instance, from French to English). This course explores questions fundamental to computer science such as which problems cannot be solved by computers, can we formalize computing as a mathematical concept without relying upon the specifics of programming languages and computing platforms, and which problems can be solved efficiently. This course will also explore various approaches to object recognition that make use of geometric techniques, these would include alignment based methods and techniques that exploit geometric invariants. Design and implementation of a significant piece of work: software, hardware or theory. Entropy, relative entropy, and mutual information for discrete and continuous random variables. We are currently witnessing a technological paradigm shift, in which the IoT systems are increasingly deployed in society. Topics to be covered are: genome annotation and string algorithms, pattern search and statistical learning, molecular evolution and phylogenetics, functional genomics and systems level analysis. Topics covered include architectural aspects of modern GPUs, with a special focus on their streaming parallel nature, writing programs on the GPU using high level languages like Cg and BrookGPU, and using the GPU for graphics and general purpose applications in the area of geometry modeling, physical simulation, scientific computing and games. Students are expected to execute a substantial project in databases, write up a report describing their work, and make a presentation. The class will focus on an advanced topic in computational vision: recognition, vision-based navigation, 3-D reconstruction. Special emphasis will be given to unifying the different approaches to the subject coming from statistics, function approximation, optimization, pattern recognition, and neural networks. Parallel algorithms running on GPUs can often achieve up to 100x speedup over similar CPU algorithms. authoritative source of information about course offerings, option requirements, graduation requirements, Students will be required to perform significant implementations. Not Offered 2020-21. Prerequisite: Two semesters of progrmming courses, e.g., CIS 120, 121, 240. This course could be taken immediately following CIS 160. It has two distinct components, analytical and systems. ; How does web advertising work? This course is available for undergraduate students only. Over the last decade, the language has evolved to meet the needs of a variety of programming styles while supporting the ever-growing capabilities of the the .NET runtime and libraries. Enrollment limited to 30 students. Students will work in teams in two 5-week long projects. Basics of digital communications: sampling, quantization, digital modulation, matched filters, equalization. Reductions revisited, Cook-Levin Theorem, completeness, NL = co-NL. The course includes weekly laboratory exercises and projects covering the lecture material and program design. The latter are evaluated jointly by the supervisor and the reader. The third major part of the course concerns file systems, including topics such as storage devices, disk management and scheduling, directories, protection, and crash recovery. Prerequisite: Knowledge of at least one programming language. Creativity and originality are highly encouraged! Through Rails, we'll ex;ore the "culture" of web programming such as agile methodology, testing, key aspects of software engineering, using web services and APIs, and deploying to the cloud. This course covers core subject matter common to the fields of robotics, character animation and embodied intelligent agents. Prerequisites: Either CMS/CS/EE/IDS 144 or CS/IDS 142 in the preceding term, or instructor permission. ; Why do memes spread the way they do? The second quarter transitions to planning, navigation, and perception. Topics covered include summarizing data, fundamentals of survey sampling, statistical functionals, jackknife, bootstrap, methods of moments and maximum likelihood, hypothesis testing, p-values, the Wald, Student's t-, permutation, and likelihood ratio tests, multiple testing, scatterplots, simple linear regression, ordinary least squares, interval estimation, prediction, graphical residual analysis. Students will be expected to apply knowledge from other courses at Caltech in designing and implementing specific subsystems. The course will involve several programming projects which will provide students with the experience they need to program effectively in these languages. This course will focus on computer science education in K-14 settings. Introduction to the Gödel completeness and incompleteness theorems. Basic introduction to computer systems, including hardware-software interface, computer architecture, and operating systems. Familiarity with the material in CS 24 is strongly advised before attempting this course. The structure of the course will be lecture-based using small-team based exercises for evaluation. Heavy emphasis is placed on documentation, testing, and software architecture. But in reality, people are quickly solving these problems on a huge scale with clever systems and heuristics! Physically based simulation techniques allow for creation of extremely realistic special effects for movies, video games and surgical simulation systems. CIS 398 Quantum Computer and Information Science. Topics covered will include traits and generics; memory safety (move semantics, borrowing, and lifetimes); Rust's rich macro system; closures; and concurrency. Prerequisite: Previous expoure to majr concepts in linear algebra (i.e. Prerequisites: CS 2, CS 38, CS/IDS 121 and familiarity with Java, or instructor's permission. This course is closed to first and second term freshman for credit. This course would focus on powerful general techniques for designing randomized algorithms as well as specific representative applications in various domains, including approximation algorithms, cryptography and number theory, data structure design, online algorithms, and parallel and distributed computation. We will study techniques for locating machines, resources, and data (including directory systems, information retrieval indexing, ranking, and web search); and we will investigate how different architectures support scalability (and the issues they face). Specifically: - Assignments involve programming in C/C++ in the LLVM compiler infrastructure. This is a paper reading course, and students are expected to understand material directly from research articles. For BA Students: Formal Reasoning and Analysis. The topics covered vary from year to year, depending on the students and staff. This course will focus on numerical algorithms and scientific computing techniques that are practical and efficient for a number of canonical science and engineering applications. A course on computer programming emphasizing the program design process and pragmatic programming skills. This course builds up, and brings to practice, the elements of robotic systems at the intersection of hardware, kinematics and control, computer vision, and autonomous behaviors. The Senior Capstone Project is required for all BAS degree students, in lieu of the senior design course. We will discuss string algorithms, hidden markov models, dimension reduction, and machine learning (or phylogeny estimation) for biomedical problems. Freshmen standing. The challenge of the task undertaken must be consistent with the student's academic level. CIS 670 Advanced Topics in Programming Languages. Implementations focus on two orthogonal avenues: speed (for which C is used) and algorithmic thinking (for which Python is used). Deviation bounds, k-wise independence, graph problems, identity testing, derandomization and parallelization, metric space embeddings, local lemma. This course will teach the fundamentals of developing web applications using Ruby on Rails, a rapid-development web framework developed by Basecamp, and adopted by companies like Airbnb, GitHub, Bloomberg, CrunchBase, and Shopify. This course is identical to CS 38. Offered 2020-21. The details of this course change from year to year, but its purpose is to cover theoretical topics related to programming languages. It's interactive, fun, and you can do it with your friends. Both dynamically-typed and statically-typed languages will be implemented. This course will introduce students to CS in industry through weekly overview talks by alums and engineers in industry. This class will focus on case studies, c and methods of how archaeology and the past are created, presented and used in movies, museums, games, the internet, and art. If you have never programmed before, you should take CIS 110 first. Current research topics such as (may vary each year): convex relaxation of OPF, frequency regulation, energy functions and contraction regions, volt/var control, storage optimization, electric vehicles charging, demand response. The second part of the course addresses the problem of memory management; it will cover topics such as linking, dynamic memory allocation, dynamic address translation, virtual memory, and demand paging. Lecture, laboratory, and project course aimed at understanding visual information processing, in both machines and the mammalian visual system. Emphasis will be placed on both analytical and computational techniques. Over the past few decades, randomization has emerged as a powerful resource in algorithm desgin. Prerequisites: CS 2, CS 24, Ma 6 or permission from instructor. Course Listings. This course assumes some previous programming experience, at the level of a high school computer science class or CIS110. CS 101 | Intro to Computers & Information Technology 5 Credits. CIS 548 Operating Systems Design and Implementation. Thus, neurobiology is used as a motivating factor to introduce the relevant algorithms. This course is appropriate as an upper-level undergraduate CIS elective. Prerequisites: Ma 5 or equivalent, or instructor's permission. Graph-theoretic algorithms in computational biology, and randomization and computation; literature in dynamic graph algorithms, approximation algorithms, and other areas according to student interests. CIS 261 Discrete Probability, Stochastic Processes, and Statistical Inference. This course provides an introduction to fundamental concepts of distributed systems, and the design principles for building large scale computational systems. It teaches students how to program in a number of simplified languages representing the major programming paradigms in use today (imperative, object-oriented, and functional). Topics include: recursion, first-class functions, higher-order functions, algebraic data types, polymorphic types, function composition, point-free style, proving functions correct, lazy evaluation, pattern matching, lexical scoping, type classes, and modules. An honors thesis to be read by an external examiner and examined in an oral examination. Deep learning techniques now touch on data systems of all varieties. The course is intended to establish a foundation for further work in many topics in the computer science option. The emphasis is on the underlying logical and automata-theoretic concepts, the algorithmic solutions, and heuristics to cope with the high computational complexity. More senior students may wish to take the course to gain an understanding of the scope of research in computer science. Prerequisites: Ma 1b, Ph 2b or Ph 12b, CS 21, CS 38 or equivalent recommended (or instructor's permission). This class introduces students to the basics of data ethics. This course provides overview of bioinformatics and computational biology as applied to biomedical research. Overview of classical information theory, compression of quantum information, transmission of quantum information through noisy channels, quantum error-correcting codes, quantum cryptography and teleportation. CIS 189 Solving Hard Problems in Practice. Evaluation is based on regular homework assignments as well as a final project and class participation. Students must have an abstract of their Senior Project, which is approved and signed by a Project Adviser, at the end of the second week of Fall classes. Digital Circuits Analysis and Design with Complete VHDL and RTL Approach. Individual research project, carried out under the supervision of a member of the computer science faculty (or other faculty as approved by the computer science undergraduate option representative). See also: UW Course Catalog. Emphasis will not be on syntax or parsing but on the essential differences in these languages and their implementations. Prerequisite: Familiarity with threads and concurrency, strong Java programming skills. This course focuses on the challenges encountered in building Internet and web systems: scalability, interoperability (of data and code), security and fault tolerance, consistency models, and location of resources, services, and data. This is a challenging course that introduces the basic ideas behind computer graphics and some of its fundamental algorithms. The course will involve written assignments, examinations, and programming assignments.. Students will work in teams to design and implement networked systems in layers, from routing protocols, transport protocols, to peer-to-peer networks. The course will focus on basic foundational concepts underpinning and motivating modern machine learning and data mining approaches. CIS 450 Database and Information Systems. Additional topics include other query languages based on the relational calculi, data-warehousing and dimensional analysis, writing and using stored procedures, working with hierarchies and graphs within relational databases, and an overview of transaction processing and query evaluation. This course will illuminate the issues associated with programming at this level and will cover issues such as explicit memory management, pointers, the compilation process and debugging. Enrollment is by permission of the instructor. Given in alternate years; Not Offered 2020-21. Permutation groups; counting nonisomorphic structures. This course introduces students to various tools (source control, automated build systems, programming environments, test automation, etc.) Inherently complex problems of exponential and superexponential difficulty. Upon completion of the course, this application will be deployed and made accessible to the public. The course will include a series of projects that implements life-critical embedded systems (e.g., pacemaker, infusion pumps, closed-loop medical devices). And BS degrees, as well as combined BA/MS and BS/MS degrees introduce students CS... Include code reviews, testing, and mutual information, source and channel coding.! 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