(See also Joint Major in Computer Science and Mathematics)

Professors Erlinger (Chair), Alvarado, Dodds, Keller, Kuenning, Libeskind-Hadas, O’Neill, Stone and Sweedyk.

Computer science is an exciting and rapidly-evolving discipline with components of design, logic, mathematics, engineering and philosophy. The role of computer science can be characterized as providing the logical infrastructure for the modern, information-based society.

The Harvey Mudd College Computer Science major, established in 1992, provides a strong foundation in the fundamental principles and concepts of computer science through a blend of experimentation, theory and design. Our students are well-prepared to make contributions to the field of computing, to science within a computational framework, and to society in general through the development of tools and technologies that can have a significant, positive, societal impact.

Each computer science major participates in a year-long Computer Science Clinic project, addressing real-world problems provided by sponsors from industry and research laboratories. A Clinic project typically comprises three to five students, supervised by a faculty member and a liaison from the sponsor, working on a project from “concept to product.”

Our students may also engage in research with our faculty during both the academic year and the summer. Examples of recent student involvement in research include the design, analysis, and simulation of next generation optical networks; the design of a system that recognizes and correctly interprets human sketches of digital logic circuits on tablet computers; the design and implementation of new programming languages; research into issues of applying and deploying network security; and the design of vision algorithms for autonomous robots.

Graduates of the Computer Science Department have gone on to work for a diverse set of employers and, in some cases, have started their own companies. Some employers that have hired our graduates in recent years include Apple Computer, FICO, Google, Green Hills Software, Hewlett-Packard, IBM, JPL, Lawrence Livermore National Laboratory, Lockheed-Martin, Microsoft, Qualcomm, Rockwell, Stanford Linear Accelerator, Sun Microsystems and The Aerospace Corporation.

A significant fraction of our majors have gone on to graduate study. Some of the graduate programs where our students have enrolled include Caltech, Carnegie Mellon University, Cornell, Georgia Institute of Technology, Stanford, UC Berkeley, UC Davis, UCLA, UC San Diego, University of North Carolina, University of Illinois at Urbana-Champaign, University of Texas, University of Washington and University of Wisconsin. Our graduates have done advanced study in areas such as algorithm design and analysis, logical foundations of computer science, software engineering, computer graphics, networking, distributed systems, mobile computing, performance analysis, programming languages, computer architecture, computer operating systems, parallel computing, artificial intelligence, computer vision, robotics, speech understanding, virtual reality, artificial life, neural networks, human-computer interfaces and telecommunications. Most of these areas are introduced in courses at HMC.

All students at Harvey Mudd College are required to fulfill Computer Science 5 (Introduction to Computer Science) which provides an exposure to some major concepts in the discipline including functional programming, object-oriented programming, digital logic and computer organization, computability theory and societal issues. The computer science major continues with the foundation courses, starting with Computer Science 60 (Principles of Computer Science) which provide a broad exposure to many areas of computer science and further develop fundamental competence in programming, logic, algorithm analysis and computer structure. Mathematics 55 is taken to develop skills in discrete mathematics that are needed for advanced computer science areas. Computer Science 70 (Data Structures and Program Development) improves the students’ depth of programming competence and diversifies the set of data structures and corresponding analysis techniques to which the computer science student is exposed. Computer Science 81 (Computability and Logic) introduces the mathematical foundations of computer science, particularly logic, automata and computability theory, and demonstrates the applications of the aforementioned areas to problems of practical significance.

Building on the foundation courses are the kernel courses. Computer Science 105 (Computer Systems) develops a deep understanding of computer structure and its relationship to correct and efficient program implementation. Computer Science 121 (Software Development) focuses on requirements analysis and specification techniques for large software systems and the project management skills needed to develop such systems. Computer Science 131 (Programming Languages) investigates concepts underlying a wide variety of modern programming languages. Computer Science 140 (Algorithms) develops fundamental skills needed to perform comparative analysis of algorithms and to enable the synthesis of new algorithms. The broad array of computer science electives (over 20 elective and seminar courses) allows students to achieve more specialization in areas of personal interest.

The Computer Science Clinic provides a way of putting many of the acquired skills into practice.  Examples of recent Computer Science Clinic projects are:

• A new system for the management of rocket launch countdown clocks;

• An extensible interface for current and future insulin pumps, glucose sensors and related diabetes technology;

• A computer security tool based on the biological immune system paradigm;

• An extensible software architecture that enables satellite anomalies to be detected and displayed in visual form;

• A simulation model of the GPS ground network and verification of that model through available data;

Sponsors of Computer Science Clinic projects have included Boeing, FICO (Fair Isaac), Google, GTE, IBM, Jet Propulsion Laboratory, Microsoft, Octel Communications, Optivus Technology, Q, Sandia National Laboratories, Teradyne, and The Aerospace Corporation.

The final element of the major is the Computer Science Colloquium, which features speakers drawn from both industry and academia who present results of their current research. Recent colloquium speakers have come from a variety of companies, research labs, and universities.

DEGREE REQUIREMENTS. A computer science major must complete the following courses:

Computer Science Foundation:

Computer Science 60. Principles of Computer Science, or
Computer Science 42. Principles and Practice of  Computer Science
Mathematics 55. Discrete Mathematics
Computer Science 70. Data Structures and Program Development
Computer Science 81. Computability and Logic

Computer Science Kernel:

Computer Science 105: Computer Systems
Computer Science 121: Software Development
Computer Science 131: Programming Languages
Computer Science 140: Algorithms

Three Computer Science Electives:

Computer Science 124: User Interface Design
Computer Science 125: Computer Networks
Computer Science 132: Compiler Design
Computer Science 133: Databases
Computer Science 134: Operating Systems: Design and Implementation
Computer Science 135: File Systems
Computer Science 136: Advanced Computer Architecture
Computer Science 141: Advanced Topics in Algorithms
Computer Science 142: Complexity Theory
Computer Science 144: Scientific Computing
Computer Science 147: Computer Systems Performance Analysis
Computer Science 151: Artificial Intelligence
Computer Science 152: Neural Networks
Computer Science 153: Computer Vision
Computer Science 154: Robotics
Computer Science 155: Computer Graphics
Computer Science 156: Parallel and Real-Time Computing
Computer Science 157: Computer Animation

Students may substitute electives in one or more computer science-related areas, such as in engineering or mathematics, with the consent of their faculty adviser. Computer Science 186 (Computer Science Research II) can be counted as an elective for the major and requires Computer Science 185 (Computer Science Research I) as a prerequisite. Other research or project courses cannot normally be counted as electives for the major.

Both of the following: Two semesters of Computer Science 183, 184 (Clinic) and four semesters of Computer Science 193-196 (Colloquium).

Computer Science Colloquium is only required when students are in residence at HMC. Study abroad students are excused from the colloquium requirement during their time away from the HMC campus.

Concentration in Computer Engineering
Students frequently ask about the possibility of pursuing a computer engineering major at HMC. As the Department of Engineering offers a non-specialized engineering degree,
students interested in computer engineering may wish to major in computer science. While the courses offered in the Computer Science Department are focused primarily on systems and software, appropriate engineering courses may be counted toward the elective course requirements of the CS major. In addition, the computer science major allows flexibility for taking additional electives beyond the major requirements, and these may be taken in engineering as well. Thus an HMC computer science major may graduate with a hardware or engineering emphasis. Engineering courses that are generally accepted as CS technical electives include E85: Digital Electronics and Computer Engineering; E115: Project Management; E151: Engineering Electronics; E155: Microprocessor-based Systems: Design and Applications; E161: Computer Image Processing and Analysis; and E158: Introduction to CMOS VLSI Design.

Computer Science Majors from the Other Claremont Colleges
Pomona College offers an undergraduate major in computer science and there is close cooperation between the Pomona and HMC Computer Science Departments.

HMC welcomes Computer Science majors from the other Claremont Colleges. Students from the other colleges who desire to major in Computer Science at Harvey Mudd College should inform the Chair of the Computer Science Department of their plans so that they may be assigned an appropriate adviser.

The HMC Computer Science major assumes significant material included in the HMC Technical Core. In particular, it is assumed that students have taken courses in calculus, linear algebra and differential equations. Part of the advising process for an off-campus student involves identifying the courses that the student should take before enrolling in HMC Computer Science courses.

COMPUTER SCIENCE COURSES (Credit hours follow course title)

5. Introduction to Computer Science (3)
Alvarado, Dodds, Kuenning, Libeskind-Hadas. 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. (Fall)

6. Introduction to Biology and Computer Science (3)
Bush (Biology), Dodds, Libeskind-Hadas. First course in a two-course series that combines Biology 52 and Computer Science 5. Successful completion of this course satisfies the Computer Science 5 core requirement. Restricted enrollment for first-year students. (Fall)

42. Principles and Practice of Computer Science (3)
Alvarado, Keller. 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. Successful completion of this course satisfies the Computer Science 5 core requirement and Computer Science 60 coursework. Prerequisite: permission of instructor. (Fall)

60. Principles of Computer Science (3)
Alvarado, Dodds, Keller, Libeskind-Hadas. 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. Those who have completed Computer Science 42 cannot take Computer Science 60. Prerequisites: Computer Science 5 and one semester of calculus. (Fall and Spring)

70. Data Structures and Program Development (3)
Kuenning, O’Neill, Stone. 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 building programs for a variety of applications. Prerequisites: Computer Science 60 or 42. (Fall and Spring)

81. Computability and Logic (3)
Keller, Bull (Pomona), Sweedyk. 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. Prerequisites: Computer Science 60 or 42, Mathematics 55. (Fall and Spring)

105. Computer Systems (3)
Erlinger, Kuenning, Bull (Pomona). 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. Prerequisite: Computer Science 70. (Fall and Spring)

121. Software Development (3)
Keller, Sweedyk. 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. Prerequisite: Computer Science 70. (Fall and Spring)

124. User Interface Design (3)
Alvarado. 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. Prerequisite: Computer Science 5 or 42. (Spring, alternate years)

125. Computer Networks (3)
Erlinger. Principles and analysis techniques for internetworking. Analysis of networking models and protocols. Presentation of computer communication with emphasis on protocol architecture. Prerequisite: Computer Science 105. (Fall)

131. Programming Languages (3)
Bruce (Pomona), O’Neill, Stone. 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. Prerequisite: Computer Science 70 and 81. (Fall and Spring)

132. Compiler Design (3)
Stone. The theory, design, and implementation of compilers and interpreters. The interaction between compiler design and run-time organization. Logistics of porting to new hardware. Prerequisites: Computer Science 105 and 131. (Spring, alternate years)

133. Databases (3)
Keller. 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 databases. Prerequisites: Computer Science 70 and 81 (131 recommended). (Fall, alternate years)

134. Operating Systems: Design and Implementation (3)
O’Neill. Design and implementation of operating systems, including processes, memory management, synchronization, scheduling, protection, file systems 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 trade-offs; optimization; caching; and managing large code bases. Group projects provide experience in working with and extending a real operating system. Prerequisite: Computer Science 105. (Spring, alternate years)

135. File Systems (3)
Kuenning. 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. Prerequisites: Computer Science 105. (Fall, alternate years)

136. Advanced Computer Architecture (3)
Kuenning. 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. Prerequisite: Computer Science 105. (Spring, alternate years)

140. Algorithms (3) (Also listed as Mathematics 168)
Libeskind-Hadas, Pippenger (Mathematics), Chen (Pomona). 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. Prerequisite: Computer Science 70 and Mathematics 55. (Both semesters. Students taking the course as Mathematics 168 have slightly different prerequisites.)

141. Advanced Topics in Algorithms (3)
Libeskind-Hadas. 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. Prerequisite: Computer Science 140/Mathematics 168. (Fall, alternate years)

142. Complexity Theory (3) (Also listed as Mathematics 167)
Libeskind-Hadas, Pippenger (Mathematics). 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. Prerequisite: Computer Science 81. (Fall, alternate years)

144. Scientific Computing (3) (Also listed as Mathematics 164)
de Pillis (Mathematics), Yong (Mathematics). 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. Prerequisites: Mathematics 64 and Computer Science 60 or 42. (Spring)

147. Computer Systems Performance Analysis (3)
Kuenning. 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 queuing theory. Simulation methods. Project in performance measurement. Typical projects include measurement of databases, theorem provers, file systems, networks, OS kernels and computer processors. Prerequisites: Mathematics 62 and Computer Science 70. (Spring, alternate years)

151. Artificial Intelligence (3)
Alvarado, Sood (Pomona). 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. Prerequisite: Computer Science 81 (Computer Science 131 recommended). (Fall and Spring)

152. Neural Networks (3)
Keller. 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. Prerequisites: Computer Science 60 or 42 and Mathematics 63. (Fall)

153. Computer Vision (3)
Alvarado, Dodds. 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. Prerequisite: Computer Science 70. (Fall, alternate years)

154. Robotics (3)
Dodds. Introduction to robotics from a behavioral perspective. Topics span from sensor operation and low-level actuator control to architectures and algorithms for accomplishing tasks. 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. Prerequisites: Computer Science 70 or permission of instructor. (Spring, alternate years)

155. Computer Graphics (3)
Sweedyk. Geometric models for visual output. Rastering. Three-dimensional volume and surface modeling. Reflectance and illumination models. Texturing and shading. Color and animation. Prerequisites: Mathematics 63, Computer Science 70 and 140. (Fall)

156. Parallel and Real-Time Computing (3)
Keller, Chen (Pomona). Characteristics and applications for parallel and real-time systems. Specification techniques, algorithms, architectures, languages, design and implementation. Prerequisites: Computer Science 105 and 140 (Computer Science 131 recommended). (Spring, alternate years)

157. Computer Animation (3)
Sweedyk. 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 animation. Prerequisite: Computer Science 155. (Spring, alternate years)

181, 182. Computer Science Seminar (3)
Staff. Advanced topics of current interest in computer science. Prerequisite: Permission of instructor. (Fall and Spring)

183, 184. Computer Science Clinic I, II (3)
Staff. Team project in computer science, with corporate affiliation. Prerequisite: Computer Science 121.

185. Computer Science Research I (2-3)
Staff. An independent research project under faculty supervision. The course also has regular class meetings that address research methods and presentation skills. Prerequisite: permission of instructor. (Fall)

186. Computer Science Research II (3)
Staff. A continuation of independent research carried out in Computer Science 185 culminating in a research paper and oral presentation. Prerequisite: Computer Science 185. (Spring)

189. Programming Practicum (1)
Dodds, Stone. 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. Prerequisite: Computer Science 5 or 42 or permission of instructor. (May be repeated for
elective credit up to three times.) (Fall and Spring)

191, 192. Computer Science Project I, II (1-3)
Staff. 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. Prerequisite: permission of instructor.

193, 194, 195, 196. Computer Science Colloquium (0)
Staff. 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, both semesters while in residence. All majors welcome. No credit.

197, 198. Advanced Problems in Computer Science (1-3)
Staff. Independent study in a field agreed upon by student and a faculty member. Prerequisite: permission of instructor.