E6: Mechanics: Elementary concepts of deformable bodies are explored, including stress-strain relations, flexure, torsion, and internal pressure. Laboratory work includes a MATLAB workshop, experiments on deformable bodies, and a truss-bridge team design competition.

E11: Electrical Circuit Analysis: The analysis of electrical circuits is introduced, including resistors, capacitors, inductors, op-amps, and diodes. Techniques are taught to solve differential equations resulting from linear circuits. Solutions will be formulated both in the time domain and in the frequency domain.

E12: Linear Physical Systems Analysis: This course builds on the mathematical techniques learned in E11 and applies them to a broad range of linear systems, including those in the mechanical, thermal, fluid, and electromechanical domains. Techniques used include Laplace Transforms, Fourier analysis, and Eigenvalue/Eigenvector methods.

E15: Fundamentals of Digital Systems: The course will introduce students to digital system theory and design techniques, including Boolean algebra, binary arithmetic, digital representation of data, gates, and truth tables. Digital systems include both combinational and sequential logic consisting of flip-flops, finite state machines, memory, and timing issues.

E41: Thermofluid Mechanics: This course introduces macroscopic thermodynamics: first and second laws, properties of pure substances, and applications using system and control volume formulation. Also introduced is fluid mechanics: development of conservation theorems, hydrostatics, and the dynamics of one-dimensional fluid motion with and without friction.

E72: Electronic Circuit Applications: This course is of interest to a broad range of students in the sciences. The student will learn the fundamentals of electronic circuit design starting with a brief survey of semiconductor devices including diodes and bipolar and field effect transistors. The course continues with op-amp applications, including instrumentation and filter design.

E78: Communication Systems: Theory and design principles of analog and digital communication systems are explored. Topics include frequency domain analysis of signals; signal transmission and filtering; random signals and noise; AM, PM, and FM signals; sampling and pulse modulation; digital signal transmission; PCM; coding; and information theory.

Ling 120: Endangered Languages: In this seminar, we address some traditional issues of concern to both linguistics and anthropology, framed in the context of the ongoing, precipitous decline in human linguistic diversity. With the disappearance of languages, cultural knowledge (including entire technologies such as ethnopharmacology) is often lost, leading to a decrease in humans' ability to manage the natural environment.

Ling 50: Syntax: We study the principles that govern how words make phrases and sentences in natural language. Much time is spent on learning argumentation skills. The linguistic skills gained in this course are applicable to the study of any modern or ancient natural language.

Economics 1: Covers the fundamentals of microeconomics and macroeconomics: supply and demand, market structures, income distribution, fiscal and monetary policy in relation to unemployment and inflation, economic growth, and international economic relations.

CS 21: Introduction to Computer Science: This course will present fundamental ideas in computer science while building skill in software development. Algorithms will be implemented as programs in a high-level programming language. Object-oriented programming and data structures will be introduced to construct correct, understandable, and efficient algorithms.

CS 35: Data Structures and Algorithms: This course completes the broad introduction to computer science begun in CPSC 021. It provides a general background for further study in the field. Topics to be covered include object-oriented programming in Java, advanced data structures (priority queues, trees, hash tables, graphs, etc.) and algorithms, and software design and verification.

CS63: Artificial Intelligence: This course will focus on a subset of these topics and specifically on machine learning, which is concerned with the problem of how to create programs that automatically improve with experience. Machine learning approaches studied will include neural networks, decision trees, genetic algorithms, and reinforcement techniques.