| source Caltech (X) |
level |
department Computer Science (X) |
CS 1 is an introduction to the automated processing of information, including computer programming. This course gives students the conceptual background necessary to understand and construct programs (i.e., specify computations, understand evaluation models, use and understand major constructs, including functions and procedures, scoping and environments, data storage, side-effects, conditionals, recursion and looping, and higher-order functions). CS 1 introduces key issues that arise in computation (e.g., universality, computability, complexity, representation, abstraction management). This course puts the components of computer science in context, serving as an overview for students specializing in computational disciplines and alerting all students to important subtleties that may arise when applying computation in their studies, research, and work. At the end of this course, students should be able to read and write (synthesize, analyze, understand) small programs (100 lines) and have the intellectual framework necessary to rapidly assimilate new computer languages as the need arises. Instructors: Pinkston, Vanier.
Score: 7.712639 Details | Listing | Web page
CS 2 is a challenging course in programming languages and computer science, emphasizing modes of algorithmic expression. The course will include such topics as performance analysis of algorithms; proofs of program correctness; recursive and higher-order procedures; data structures, including lists, trees, graphs, and arrays; objects and abstract data types. The course includes weekly laboratory exercises and written homework covering the lecture material and program design. Instructor: Barr.
Score: 7.712639 Details | Listing | Web page
CS 3 is an advanced introduction to the fundamentals of computer science and software engineering methodology. Topics will be chosen from the following: abstract data types; object-oriented models and methods; logic, specification, and program composition; abstract models of computation; probabilistic algorithms; nondeterminism; distributed algorithms and data structures. The weekly laboratory exercises allow the students to investigate the lecture material by writing nontrivial applications. Instructor: Staff.
Score: 7.712639 Details | Listing | Web page
For course description, see Mathematics.
Score: 7.712639 Details | Listing | Web page
CS 11 is a self-paced lab that provides students with extra practice and supervision in transferring their programming skills to a particular programming language; the course can be used for any language of the student’s choosing, subject to approval by the instructor. A series of exercises guide the student through the pragmatic use of the chosen language, building his or her familiarity, experience, and style. More advanced students may propose their own programming project as the target demonstration of their new language skills. Lab staff will critique the student’s technique and craftsmanship, offering expert feedback on areas for attention and helping the student with any conceptual difficulties that may arise while mastering the particular language. CS 11 may be repeated for credit of up to a total of 9 units. Instructors: Vanier, Pinkston.
Score: 7.712639 Details | Listing | Web page
This course introduces the formal foundations of computer science, the fundamental limits of computation, and the limits of efficient computation. Topics will include automata and Turing machines, decidability and undecidability, reductions between computational problems, and the theory of NP-completeness. Instructor: Umans.
Score: 7.712639 Details | Listing | Web page
Basic introduction to computer systems, including hardware-software interface, computer architecture, and operating systems. Course emphasizes computer system abstractions and the hardware and software techniques necessary to support them, including virtualization (e.g., memory, processing, communication), dynamic resource management, and common-case optimization, isolation, and naming. Instructor: Pinkston.
Score: 7.712639 Details | Listing | Web page
This course introduces techniques for the design and analysis of efficient algorithms. Major design techniques (the greedy approach, divide and conquer, dynamic programming, linear programming) will be introduced through a variety of algebraic, graph, and optimization problems. Methods for identifying intractability (via NP-completeness) will be discussed. Instructor: Kitaev.
Score: 7.712639 Details | Listing | Web page
Introduction to the basic theory and usage of relational database systems. It covers the relational data model, relational algebra, and the Structured Query Language (SQL). The course introduces the basics of database schema design and covers the entity-relationship model, functional dependency analysis, and normal forms. 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. Extensive hands-on work with SQL databases. Instructor: Pinkston.
Score: 7.712639 Details | Listing | Web page
Undergraduates must enroll for CS 47; graduates must enroll for CS 147. This course covers the advanced object-oriented programming techniques typically used in large programming projects. Fundamental programming techniques such as object design, inheritance of implementation and/or interface, and polymorphism are also discussed. Other, more advanced, programming concepts covered include smart pointers, garbage collection, object permanence, patterns, and Internet programming. Not offered 2008–09.
Score: 7.712639 Details | Listing | Web page
For course description, see Electrical Engineering.
Score: 7.712639 Details | Listing | Web page
For course description, see Electrical Engineering.
Score: 7.712639 Details | Listing | Web page
For course description, see Electrical Engineering.
Score: 7.712639 Details | Listing | Web page
For course description, see Electrical Engineering.
Score: 7.712639 Details | Listing | Web page
For course description, see Electrical Engineering.
Score: 7.712639 Details | Listing | Web page
Supervised experimental research in computer science by undergraduates. Topic must be approved by the supervisor, and a formal final report must be presented on completion of research. Graded pass/fail. Instructor: Staff.
Score: 7.712639 Details | Listing | Web page
Supervised research in computer science by undergraduates. Topic must be approved by the supervisor, and a formal final report must be presented on completion of research. Graded pass/fail. Instructor: Staff.
Score: 7.712639 Details | Listing | Web page
The topics covered vary from year to year, depending on the students and staff. Primarily for undergraduates.
Score: 7.712639 Details | Listing | Web page
For course description, see Applied and Computational Mathematics.
Score: 7.712639 Details | Listing | Web page
For course description, see Mathematics.
Score: 7.712639 Details | Listing | Web page
An introduction to the theory and practice of logic model checking as an aid in the formal proofs of correctness of concurrent programs and system designs. The specific focus is on automata-theoretic verification. The course includes a study of the theory underlying formal verification, the correctness of programs, and the use of software tools in designs. Instructor: Holzmann.
Score: 7.712639 Details | Listing | Web page
The class discusses theoretical and practical aspects of software testing and monitoring. Topics include finite state machine testing algorithms, random testing, constraint-based testing, coverage measures, automated debugging, logics and algorithms for runtime monitoring, and aspect-oriented approaches to monitoring. Emphasis is placed on automation. Students will be expected to develop and use software testing and monitoring tools to develop reliable software systems. Instructors: Groce, Havelund.
Score: 7.712639 Details | Listing | Web page
A basic course in information theory and computational complexity with emphasis on fundamental concepts and tools that equip the student for research and provide a foundation for pattern recognition and learning theory.
Score: 7.712639 Details | Listing | Web page
For course description, see Mechanical Engineering.
Score: 7.712639 Details | Listing | Web page
Operating systems, monolithic and microkernels, virtual machines. Naming, memory management, segmentation, paging, and virtual memory. File systems and I/O. Threads, processes, scheduling, locks, semaphores, and mutual exclusion. Security policies, access-control, capabilities, and language-based security. Not offered 2008–09.
Score: 7.712639 Details | Listing | Web page