Computer Science Course Descriptions

CS 5. Introduction to Computer Programming
Alvarado, Dodds, Kuenning, Libeskind-Hadas. Introduction to programming and problem solving via computer. Students are introduced to and program in Java. In this course, students explore data representation (both simple and structured data types, classes, objects), flow-control structures (if, for, recursion, subroutines), basic algorithms (sorting, searching, numeric computation), and program design. No prior programming experience required. 3 credit hours (First semester.)

CS 60. Principles of Computer Science
Dodds, Keller, and 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. Prerequisites: Computer Science 5 (or equivalent) and one semester of calculus. 3 credit hours. (Both semesters.)

CS 70. Data Structures and Program Development
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 in implementing these data structures for a variety of applications. Prerequisites: Computer Science 60. 3 credit hours. (Both semesters.)

CS 81. Computability and Logic
Keller, Bull (Pomona). 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 and Math 55. 3 credit hours. (Both semesters.)

CS 105. Computer Systems
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. 3 credit hours. (Both semesters.)

CS 121. Software Development
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. Prerequisites: Computer Science 70. 3 credit hours. (Both semesters.)

CS 124. User Interface Design
O'Neill. 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. (Second semester, alternate years).

CS 125. Computer Networks
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. 3 credit hours. (Second semester.)

CS 131. Programming Languages
O'Neill, Stone, and Bruce (Pomona). 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. 3 credit hours. (Both semesters.)

CS 132. Compiler Design
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. Prerequisite: Computer Science 105 and 131. 3 credit hours. (Second semester, alternate years.)

CS 133. Databases
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 data-bases. Prerequisite: Computer Science 70 and 81 (131 recommended). 3 credit hours. (First semester, alternate years.)

CS 134. Operating Systems: Design and Implementation
O'Neill. 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. Prerequisite: Computer Science 105. 3credit hours. (Second semester, alternate years.)

CS 136. Advanced Computer Architecture
Erlinger, 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. 3 credit hours. (Second semester, alternate years.)

CS 140. Algorithms (joint-listed as Mathematics 168).
Libeskind-Hadas, Sweedyk, 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. 3 credit hours. (Both semesters. Students taking the course as Mathematics 168 have slightly different prerequisites.)

CS 141. Advanced Topics in Algorithms
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. 3 credit hours. (First semester, alternate years.)

CS 142. Complexity Theory .
Libeskind-Hadas, Bull (Pomona). 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. 3 credit hours. (First semester.)

CS 144. Scientific Computing (joint-listed as Mathematics 164).
dePillis. 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 63 and 64, and Computer Science 60. 3 credit hours. (Second semester.)

CS 147. Computer Systems Performance Analysis
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 queueing theory. Simulation methods. Project in performance measurement. Typical projects include measurement of databases, theorem provers, file systems, networks, OS kernels, and computer processors.

CS 151. Artificial Intelligence
Alvarado. 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 (131 recommended). 3 credit hours. (Second semester.)

CS 152. Neural Networks
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 and Mathematics 63. 3 credit hours. (First semester.)

CS 153. Computer Vision
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. 3 credit hours. (Second semester, alternate years.)

CS 154. Robotics
Dodds. 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. Prerequisite: Computer Science 70. 3 credit hours. (Second semester)

CS 155. Computer Graphics
Sweedyk. Geometric models for visual output. Rastering. Three-dimensional volume and surface modeling. Reflectance and illumination models. Texturing and shading. Color and animation. Prerequisite: Computer Science 140 and Mathematics 63 (Linear Algebra). 3 credit hours. (First semester.)

CS 156. Parallel and Real-Time Computation
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 (131 recommended). 3 credit hours. (Second semester, alternate years.)

CS 157. Computer Animation
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 computer animation. Prerequisites: Computer Science 155. 3 credit hours. (Second semester, alternate years.)

CS 181, 182. Computer Science Seminar
Staff. Advanced topics of current interest in computer science. Prerequisite: permission of instructor. 3 credit hours. (Both semesters.)

CS 183, 184. Computer Science Clinic I, II
Staff. Team project in computer science, with corporate affiliation. Prerequisites: Computer Science 121. 3 credit hours.

CS 185, 186. Senior Research Project I, II
Staff. Projects involving original research under faculty supervision. 1-3 credit hours.

CS 189. Programming Practicum.
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. 1 credit hour. (May be repeated for elective credit up to 3 times.) (Both semesters.)

CS 191, 192. Computer Science Project I, II.
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. 1-3 credit hours per semester.

CS 193, 194, 195, 196. Computer Science Colloquium.
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 any semester in residence at HMC. Study abroad students should coordinate this requirement with the Computer Science Faculty member who is organizing colloquium. 0 credit hours.

CS 197, 198. Advanced Problems in Computer Science.
Staff. Independent study in a field agreed upon by student and a faculty member. 1-3 credit hours.