Topics include implementation and analysis of skip lists, trees, hashing, and heaps as well as various algorithms (including string matching, parallel sorting, parallel prefix). Topics include synchronous design, state machine design, ALU and CPU design, application-specific parallel computer design, design for testability, PALs, FPGAs, VHDL, standard cells, timing analysis, fault vectors, and fault grading. This course is intended for students who have already taken a data structures course at the level of CS 2. Topics include configuration space, sample-based planners, A* and D* algorithms, to achieve collision-free motions. This course is intended to provide a hands-on experience with interesting things to do on images/videos. This semester's project will be a peer-to-peer implementation of a Googe-style search engine, including distributed, scalable crawling; indexing with ranking; and even PageRank. The intent of the course is to provide the student with a solid technical foundation for developing, animating and controlling articulated systems used in interactive computer games, virtual reality simulations and high-end animation applications. program is appropriate for students desiring a strong concentration in the core areas of computer science—algorithms, programming languages, and systems—with more courses in computer science, computer engineering, and computational media; this program also allows for a few electives outside of science and engineering. Programming sensor networks and cloud computing applications. Topics include: discrete models, regression models, hierarchical models, model comparison, and MCMC methods. Topics discussed may include probabilistic and counterfactual treatments of causation, the role of statistical evidence and experimentation in causal inference, and the deductive-nomological model of explanation. CS 11 may be repeated for credit of up to a total of nine units. Not offered 2020-21. The course provides students with an opportunity to experience combined team-based design, engineering, and entrepreneurship. Inherently complex problems of exponential and superexponential difficulty. This Freshman Seminar is designed to be a very introductory exposition about Quantum Computation and Quantum Information Science. This course is designed to provide a comprehensive overview to computer graphics techniques in 3D modeling, image synthesis, and rendering. Every year the course and projects focus on a particular emerging technology theme. Current Topics in Theoretical Computer Science. Prerequisites: CS/CNS 171, or 173, or 174. It presents selected topics from these domains, focusing on their integration into a full sense-think-act robot. Specifically, it will be assumed that the students will know: Set Theory, Mathematical Induction, Number Theory, Functions, Equivalence Relations, Partial-Order Relations, Combinatorics, and Graph Theory at the level currently covered in CIS 160. Lower Division. Assignments will include problems drawn from fields such as graphics, numerics, networking, and games. Topics covered include: geometric coordinate systems and transformations; quaternions; parametric curves and surfaces; forward and inverse kinematics; dynamic systems and control; computer simulation; keyframe, motion capture and procedural animation; behavior-based animation and control; facial animation; smart characters and intelligent agents. Numerical Algorithms and their Implementation. This course investigates computation by neurons. An important goal of the course is not simply to discuss issues and solutions, but to provide hands-on experience with a substantial implementation project. Introduction to Computer Programming. The topics include finite automata and regular languages, context-free grammars and pushdown automata, Turing machines and undecidability, tractability and NP-completeness. The class will include a tutorial introduction to the topic, an exploration of relevant recent literature, and a project involving the design, implementation, and testing of a vision system. This course examines the architecture and capabilities of modern GPUs. Continuation of CIS 400. The students will become acquainted with encoding and decoding algorithms, design principles and performance evaluation of codes. The Independent Study should not be used for ongoing research towards a thesis, for which the CIS 999 designation should be used. 3 units (2-0-1), 6 units (2-0-4), or 9 units (2-0-7) first term; 6 units (2-3-1), 9 units (2-6-1), or 12 units (2-9-1) second and third terms; units according to project selected: This course presents the fundamentals of modern multidisciplinary systems engineering in the context of a substantial design project. They will learn how to gather data, store it in appropriate data structures and then either write their own functions or use libraries to analyze and then display the salient information in that data. Introduction to Computer Programming is the first course in our series introducing students to computer science. Topics will include automata and Turing machines, decidability and undecidability, reductions between computational problems, and the theory of NP-completeness. Prerequisite: Previous expoure to majr concepts in linear algebra (i.e. Implementations focus on two orthogonal avenues: speed (for which C is used) and algorithmic thinking (for which Python is used). Quantitative Reasoning for B.S. Introduction to the basic theory and usage of relational database systems. The purpose of this course is to introduce undergraduate students in computer computer science and engineering to quantum computers (QC) and quantum information science (QIS). Students will be expected to apply knowledge from other courses at Caltech in designing and implementing specific subsystems. Design and implementation of a significant piece of work: software, hardware or theory. CIS 515 Fundamentals of Linear Algebra and Optimization. The second quarter transitions to planning, navigation, and perception. The course develops the core concepts of robotics. The students will master the basic algorithms used for lossless and lossy compression of digital and analog data and the major ideas behind coding for flash memories. Not offered 2020-21. Different topics selected each offering; e.g., NL generation, question-answering, information extraction, machine translation, restricted grammar formalisms, computational lexical semantics, etc. Subject matter covered: differential geometry of curves and surfaces, classical exterior calculus, discrete exterior calculus, sampling and reconstruction of differential forms, low dimensional algebraic and computational topology, Morse theory, Noether's theorem, Helmholtz-Hodge decomposition, structure preserving time integration, connections and their curvatures on complex line bundles. 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. This is an introductory course to Computer Vision and Computational Photography. The intent of this course is to provide a basic understanding of a wide variety of programming paradigms, such as logic programming, functional programming, concurrent programming, rule-based programming, and others. ; How do search engines work? Concurrent distributed operation is emphasized. 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. Knowledge of vector geometry is useful. Topics covered include linear and logistic regression, SVMs, PCA and dimensionality reduction, EM and HMMs, and deep learning. This course WILL NOT provide a broad survey of the field nor teach specific tools but focus on a deep understanding of a small set of topics. At the end of the first semester, students are required to submit an intermediate report and give a class presentation describing their project and progress. The theory of quantum information and quantum computation. (1 course required) Science, Math, & Technology (1 course required) Social Sciences (1 course required) Program requirements that also fulfill General Education Program Requirements. This is the second computer organization course and focuses on computer hardware design. Topics will include: software development lifecycle; agile and test-driven development; source control and continuous integration; requirements analysis; object-oriented design and testability; mobile and/or web application development; software testing; refactoring ; and software quality metrics. Prerequisite: Students should have a good knowledge of object-oriented programming (C++) and basic familiarity with linear algebra and physics. Prerequisites: CS 155; strong background in statistics, probability theory, algorithms, and linear algebra; background in optimization is a plus as well. Design and implementation of a significant piece of work: software, hardware or theory. Catalog; Courses; Computer Science; Computer Science Courses View/Print PDF. Students will learn laboratory techniques including fluorescence spectroscopy and atomic force microscopy, and will use software tools and program in MATLAB or Mathematica. Given in alternate years; Not offered 2020-21. Polynomial deterministic vs. nondeterministic algorithms, NP-complete problems and the P = NP question. Network information theory, including multiuser data compression, multiple access channels, broadcast channels, and multiterminal networks. 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. This course covers core subject matter common to the fields of robotics, character animation and embodied intelligent agents. This course is suited for students from all Engineering backgrounds, who have the basic knowledge of linear algebra and programming, and a lot of imagination. It will use the Python programming language and will not assume previous programming experience. We are currently witnessing a technological paradigm shift, in which the IoT systems are increasingly deployed in society. While traditional image processing techniques will be discussed to provide context, the emphasis will be on cutting edge aspects of all areas of image analysis (including registration, segmentation, and high-dimensional statistical analysis). Topics may vary depending on instructor. This course presents the use of logic and formal reasoning to prove the correctness of sequential and concurrent programs. Topics covered include data structures, including lists, trees, and graphs; implementation and performance analysis of fundamental algorithms; algorithm design principles, in particular recursion and dynamic programming; Heavy emphasis is placed on the use of compiled languages and development tools, including source control and debugging. If the required courses and preparations would not satisfy a course major, additional computer science courses must be taken to meet course major requirements. No permission from the instructor is needed. For master's students who have taken ten course units and need only to complete the writing of a thesis or finish work for incompletes in order to graduate. Deviation bounds, k-wise independence, graph problems, identity testing, derandomization and parallelization, metric space embeddings, local lemma. This course covers a variety of advanced topics in machine learning, such as the following: statistical learning theory (statistical consistency properties of surrogate loss minimizing algorithms); approximate inference in probabilistic graphical models (variational inference methods and sampling-based inference methods); structured prediction (algorithms and theory for supervised learning problems involving complex/structured labels); and online learning in complex/structured domains. ; How does web advertising work? All of these problems are provably hard -- no one has a fast algorithm to solve them. The use of logical formalisms in Computer Science is dominated by a fundamental conflict: expressiveness vs. algorithmic tractability. May not be repeated. Given in alternate years. Graphs: paths, trees, circuits, breadth-first and depth-first searches, colorings, matchings. This course will focus on (1) How to boost efficiency and reliability in large networks; the role of network coding, distributed storage, and distributed caching; (2) How to manage wireless access on a massive scale; modern random access and topology formation techniques; and (3) New vistas in big data networks, including distributed computing over networks and crowdsourcing. This course studies how they work and the "big" ideas behind our networked lives. Notes: No Prior Physics, Mathematics, or Computer Science Experience required. The latter are evaluated jointly by the supervisor and the reader. Not offered 2020-21. 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