Energy is the number one contributor to climate change and has significant consequences for our society, political system, economy, and environment. Energy as a fundamental driver of human development and opportunity. Fundamentals of each energy resource -- including significance and potential, drivers and barriers, policy and regulation, and social, economic, and environmental impacts and will be able to put this in the context of the broader energy system. Both depletable and renewable energy resources are covered, including oil, natural gas, coal, nuclear, biomass and biofuel, hydroelectric, wind, solar thermal and photovoltaics (PV), geothermal, and ocean energy, with cross-cutting topics including electricity, storage, climate change and greenhouse gas emissions (GHG), sustainability, green buildings, energy efficiency, transportation, and the developing world.

Quantitative evaluation of technologies and techniques for reducing energy demand of residential-scale buildings. Heating and cooling load calculations, financial analysis, passive-solar design techniques, water heating systems, photovoltaic system sizing for net-zero-energy all-electric homes.

This course will describe the background on existing energy storage solutions being used on the electric grid and in vehicles with a primary focus on batteries and electrochemical storage. It will discuss the operating characteristics, cost and efficiency of these technologies and how tradeoff decisions can be made. The course will describe the system-level integration of new storage technologies, including chargers, inverters, battery management systems and controls, into the existing vehicle and grid infrastructure. Specific focus will be given to the integration of electric vehicle charging combined with demand-side management, scheduled renewable energy absorption and local grid balancing. This course may be taken for 3 units; or 4 units if taken with the optional laboratory session.

What are the theoretical limits of computing power? What problems can be solved with computers? Which ones cannot? And how can we reason about the answers to these questions with mathematical certainty? This course explores the answers to these questions and serves as an introduction to discrete mathematics, computability theory, and complexity theory. At the completion of the course, students will feel comfortable writing mathematical proofs, reasoning about discrete structures, reading and writing statements in first-order logic, and working with mathematical models of computing devices. Throughout the course, students will gain exposure to some of the most exciting mathematical and philosophical ideas of the late nineteenth and twentieth centuries. Specific topics covered include formal mathematical proofwriting, propositional and first-order logic, set theory, binary relations, functions (injections, surjections, and bijections), cardinality, basic graph theory, the pigeonhole principle, mathematical induction, finite automata, regular expressions, the Myhill-Nerode theorem, context-free grammars, Turing machines, decidable and recognizable languages, self-reference and undecidability, verifiers, and the P versus NP question. Students with significant proofwriting experience are encouraged to instead take CS154. Students interested in extra practice and support with the course are encouraged to concurrently enroll in CS103A. Prerequisite: CS106B or equivalent. CS106B may be taken concurrently with CS103. This is a 5 unit course. Matriculated Stanford graduate students are allowed to enroll in it for 3, 4 or 5 units but must still do the standard 5 units of coursework.

Introduction to the engineering of computer applications emphasizing modern software engineering principles: program design, decomposition, encapsulation, abstraction, and testing. Emphasis is on good programming style and the built-in facilities of respective languages. Uses the Python programming language. No prior programming experience required. May be taken for 3 units by Stanford graduate students; all other students enroll for 5 units. Discussion sections assigned the first week of class via CS 198 website; do not enroll in sections through Axess.

Abstraction and its relation to programming. Software engineering principles of data abstraction and modularity. Object-oriented programming, fundamental data structures (such as stacks, queues, sets) and data-directed design. Recursion and recursive data structures (linked lists, trees, graphs). Introduction to time and space complexity analysis. Uses the programming language C++ covering its basic facilities. May be taken for 3 units by Stanford graduate students; all other students enroll for 5 units. Discussion sections assigned the first week of class via CS 198 website; do not enroll in sections through Axess.

Introduction to the fundamental concepts of computer systems. Explores how computer systems execute programs and manipulate data, working from the C programming language down to the microprocessor. Topics covered include: the C programming language, data representation, machine-level code, computer arithmetic, elements of code compilation, memory organization and management, and performance evaluation and optimization. Matriculated Stanford graduate students may enroll for 3, 4 or 5 units; everyone else must take the course for 5 units. All students do 5 units worth of work, including Stanford graduate students enrolled for 3 or 4 units.

Topics include: counting and combinatorics, random variables, conditional probability, independence, distributions, expectation, point estimation, and limit theorems. Applications of probability in computer science including machine learning and the use of probability in the analysis of algorithms. Matriculated Stanford graduate students may enroll for 3, 4 or 5 units; everyone else must take the course for 5 units. All students do 5 units worth of work, including Stanford graduate students enrolled for 3 or 4 units.

Introductory prerequisite course in the computer graphics sequence introducing students to the technical concepts behind creating synthetic computer generated images. Focuses on using OpenGL to create visual imagery, as well as an understanding of the underlying mathematical concepts including triangles, normals, interpolation, texture mapping, bump mapping, etc. Course will cover fundamental understanding of light and color, as well as how it impacts computer displays and printers. Class will discuss more thoroughly how light interacts with the environment, constructing engineering models such as the BRDF, plus various simplifications into more basic lighting and shading models. Also covers ray tracing technology for creating virtual images, while drawing parallels between ray tracers and real world cameras to illustrate various concepts. Anti-aliasing and acceleration structures are also discussed. The final class mini-project consists of building out a ray tracer to create visually compelling images. Starter codes and code bits will be provided to aid in development, but this class focuses on what you can do with the code as opposed to what the code itself looks like. Therefore grading is weighted toward in person "demos" of the code in action - creativity and the production of impressive visual imagery are highly encouraged. Matriculated Stanford graduate students may enroll for 3, 4 or 5 units; everyone else must take the course for 5 units. All students do 5 units worth of work, including Stanford graduate students enrolled for 3 or 4 units.

Worst and average case analysis. Recurrences and asymptotics. Efficient algorithms for sorting, searching, and selection. Data structures: binary search trees, heaps, hash tables. Algorithm design techniques: divide-and-conquer, dynamic programming, greedy algorithms, amortized analysis, randomization. Algorithms for fundamental graph problems: minimum-cost spanning tree, connected components, topological sort, and shortest paths. Possible additional topics: network flow, string searching. Matriculated Stanford graduate students may enroll for 3, 4 or 5 units; everyone else must take the course for 5 units. All students do 5 units worth of work, including Stanford graduate students enrolled for 3 or 4 units.