An introduction to computing using Python and multi-media. Students will be able to read, understand, modify, and assemble programs that achieve useful communication tasks: Image manipulation, sound synthesis and editing, text (e.g., HTML) creation and manipulation, and digital video effects. Students will learn useful computing skills, including database concepts.

Introduction to elements of computer science. Students learn general computational problem-solving techniques and gain experience with the design, implementation, testing and documentation of programs in a high-level language. In addition, students learn to design digital devices, understand how computers work, and learn to program a computer in its own machine language. Finally, students are exposed to ideas in computability theory. The course includes discussions of societal and ethical issues related to computer science.

Students learn to design and develop 2D computer games. Students are exposed to core computer science topics including data structures, artificial intelligence algorithms, computer graphics, software architecture and design, user interface design, and computer simulation. The course culminates in a team game project in which students learn to use modern software project management practices and principles. Cannot be used as a CS or Math/CS technical elective.

Accelerated breadth-first introduction to computer science as a discipline for students (usually first-year) who have some programming background. Computational models of functional, object-oriented, and logic programming. Data structures and algorithm analysis. Computer logic and architecture. Grammars and parsing. Regular expressions. Computability. Extensive practice constructing applications from principles, using a variety of languages.

Introduction to principles of computer science. Information structures, functional programming, object-oriented programming, grammars, logic, logic programming, correctness, algorithms, complexity analysis, finite-state machines, basic processor architecture, and theoretical limitations.

Abstract data types including priority queues, dynamic dictionaries, and disjoint sets. Efficient data structures for these data types, including heaps, self-balancing trees, and hash tables. Analysis of data structures including worst-case, average-case, and amortized analysis. Storage allocation and reclamation. Secondary storage considerations. Extensive practice in implementing these data structures for a variety of applications.

An introduction to some of the mathematical foundations of computer science, particularly logic, automata, and computability theory. Develops skill in constructing and writing proofs, and demonstrates the applications of the aforementioned areas to problems of practical significance.

An introduction to computer systems. In particular the course investigates data representations, machine level representations of programs, processor architecture, program optimizations, the memory hierarchy, linking, exceptional control flow (exceptions, interrupts, processes, and Unix signals), performance measurement, virtual memory, system-level I/O, and basic concurrent programming. These concepts are supported by a series of hands-on lab assignments.

Rigorous introduction to the technological and managerial discipline concerned with the design and implementation of large software systems. Techniques for software specification, design, verification, and validation. Formal methods for proving the correctness of programs. Student teams design, implement, and present a substantial software project.

This course introduces students to issues in the design, implementation, and evaluation of human-computer interfaces, with emphasis on user-centered design and graphical interfaces. Students will learn skills that aid them in choosing the right user interaction technique and developing an interface that is well-suited to the people for whom it is designed.

Principles and analysis techniques for internetworking. Analysis of networking models and protocols. Presentation of computer communication with emphasis on protocol architecture.

A thorough examination of issues and features in language design and implementation including language-provided data structuring and data-typing, modularity, scoping, inheritance, and concurrency. Compilation and run-time issues. Introduction to formal semantics.

The theory, design, and implementation of compilers and interpreters. The interaction between compiler design and run-time organization. Logistics of porting to new hardware.

Fundamental models of databases: entity-relationship, relational, deductive, object-oriented. Relational algebra and calculus, query languages. Data storage, caching, indexing, and sorting. Locking protocols and other issues in concurrent and distributed data-bases.

Design and implementation of operating systems, including processes, memory management, synchronization, scheduling, protection, filesystems, and I/O. These concepts are used to illustrate wider concepts in the design of other large software systems, including simplicity; efficiency; event-driven programming; abstraction design; client-server architecture; mechanism vs. policy; orthogonality; naming and binding; static vs. dynamic, space vs. time, and other tradeoffs; optimization; caching; and managing large codebases. Group projects provide experience in working with and extending a real operating system.

Computer storage and file systems. Characteristics of nonvolatile storage, including magnetic disks and solid-state memories. RAID storage. Data structures used in file systems. Performance, reliability, privacy, replication, and backup. A major portion of the course is devoted to readings selected from current research in the field.

Reduced vs. complex instruction set architecture, pipelining, instruction-level parallelism, superscalar architectures, advanced memory-hierarchy design, advanced computer arithmetic, multiprocessor systems, cache coherence, interconnection networks, performance analysis and case studies.

Algorithm design, analysis, and correctness. Design techniques including divide-and-conquer and dynamic programming. Analysis techniques including solutions to recurrence relations and amortization. Correctness techniques including invariants and inductive proofs. Applications including sorting and searching, graph theoretic problems such as shortest path and network flow, and topics selected from arithmetic circuits, parallel algorithms, computational geometry, and others. An introduction to computational complexity, NP-completeness, and approximation algorithms. Proficiency with programming is expected as some assignments require algorithm implementation.

Advanced topics in the design and analysis of combinatorial algorithms. Example topics are amortized analysis of data structures, competitive analysis of on-line algorithms, matroid theory, and introduction to parallel and distributed algorithms. A significant component of the course is written and oral student presentations of material from the original literature.

Brief review of computability theory through Rice's Theorem and the Recursion Theorem followed by a rigorous treatment of complexity theory. The complexity classes P, NP, and the Cook-Levin Theorem. Approximability of NP-complete problems. The polynomial hierarchy, PSPACE-completeness, L and NL-completeness, #P-completeness. IP and Zero-knowledge proofs. Randomized and parallel complexity classes. The speedup, hierarchy, and gap theorems.

What role do algorithms play in solving real world problems? In this class we will consider general problem solving techniques, dealing with NP-completeness, and issues concerning implementation and evaluation. The topics examined may be motivated by problems in areas such as computational biology, scientific computing, and networks. There will be a research-oriented final project.

Computational techniques applied to problems in the sciences and engineering. Modeling of physical problems, computer implementation, analysis of results; use of mathematical software; numerical methods chosen from: solutions of linear and nonlinear algebraic equations, solutions of ordinary and partial differential equations, finite elements, linear programming, optimization algorithms, and fast-Fourier transforms.

Measurement and analysis of computer software and systems performance, with emphasis on methodological issues. Measurement planning and experimental design. Statistical methods for data analysis. Hypothesis testing. Effective graphical and tabular presentation of data. Common errors in performance measurement. Elementary queueing theory. Simulation methods. Project in performance measurement. Typical projects include measurement of databases, theorem provers, file systems, networks, OS kernels, and computer processors.

Knowledge representation, including rule-based systems and neural networks, learning paradigms, and philosophical challenges to artificial intelligence. Discussion of areas of current research: natural language processing, robotics, vision, cognitive modeling, case-based-reasoning.

Modeling, simulation, and analysis of artificial neural networks and their relation to biological networks. Design and optimization of discrete and continuous neural networks. Back propagation, and other gradient descent methods. Hopfield and Boltzmann networks. Unsupervised learning. Self-organizing feature maps. Applications chosen from function approximation, signal processing, control, computer graphics, pattern recognition, time-series analysis. Relationship to fuzzy logic, genetic algorithms, and artificial life.

Computational algorithms for visual perception. Image acquisition, image processing, segmentation. Representation of color, shading, texture, shape. Stereo and motion analysis. Object recognition. Relations to robotics, human perception, image databases.

Hands-on introduction to autonomous robotics. Topics span sensor operation and low-level actuator control through architectures and algorithms for accomplishing tasks. There is an emphasis on the recent success of probabilistic approaches throughout the course. The basic framework and analysis of both industrial and biologically-motivated robots are addressed. The laboratory component of the class provides experience in developing algorithms, programming, and testing a range of robot behaviors on our hardware platforms.

Geometric models for visual output. Rastering. Three-dimensional volume and surface modeling. Reflectance and illumination models. Texturing and shading. Color and animation.

Characteristics and applications for parallel and real-time systems. Specification techniques, algorithms, architectures, languages, design, and implementation.

This course introduces students to the theory and practice of computer animation. The course covers the algorithms and data structures for building and animating articulated figures and particle systems including interpolation techniques, deformations, forward and inverse kinematics, rigid body dynamics, and physically based modeling. In addition the course surveys the art, history and production of computer animation.

An exploration of concepts and methods in machine learning including decision trees, Markov models, and neural networks. Students will implement Machine Learning methods, read and discuss contemporary research articles in the space, and independently propose, research and implement a Machine Learning approach to a modern Artificial Intelligence problem.

Advanced topics of current interest in computer science.

Team project in computer science, with corporate affiliation.

An independent research project under faculty supervision. The course also has regular class meetings that address research methods and presentation skills.

A continuation of independent research carried out in Computer Science 185 culminating in a research paper and oral presentation.

This course is a weekly programming seminar, emphasizing efficient recognition of computational problems and their difficulty, developing and implementing algorithms to solve them, and the testing of those implementations. Attention is given to the effective use of programming tools and available libraries, as well as to the dynamics of team problem-solving. May be repeated for elective credit up to three times.

Participation in projects of substantial interest to computer scientists. Emphasis is on the design and implementation of computer systems for real problems. Students typically work in small teams with faculty supervision.

Oral presentations and discussions of selected topics, including recent developments in computer science. Participants include computer science majors, clinic participants, faculty members, and visiting speakers. Required for all junior and senior computer science majors any semester in residence at HMC. Study abroad students should coordinate this requirement with the Computer Science Faculty member who is organizing colloquium.

Independent study in a field agreed upon by student and a faculty member.