Energy is the number one contributor to climate change and has significant consequences for our society, political system, economy, and environment. Energy is also a fundamental driver of human development and opportunity. Students will learn the 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, hydrogen, climate change and greenhouse gas emissions (GHG), sustainability, green buildings, energy efficiency, transportation, and the developing world. The course is 3 units, which includes lecture, readings and videos, and homework assignments. This is a course for all: pre-majors and majors, with any background - no prior energy knowledge necessary. For a course that covers all of this plus goes more in-depth, check out CEE 107A/207A - ENERGY 107A/207A - EarthSys 103 Understand Energy offered in the autumn and spring quarters (students should not take both for credit). See Website for Details

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.

The course will describe the background on existing energy storage solutions being 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 control, 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.

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. CS106B may be taken concurrently with CS103.

May be taken for 3 or 4 units by Stanford graduate students.

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.

Do not enroll in discussion sections in Axess. The first week of class, students sign up for discussion sections via CS198 website. Discussion section assignments received during second week of class when sections start.

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.

Do not enroll in discussion sections in Axess. The first week of class, students sign up for discussion sections via CS198 website. Discussion section assignments received during second week of class when sections start.

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.

May be taken for 3 or 4 units by Stanford grad students.

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.

May be taken for 3 or 4 units by Stanford grad students.

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.

May be taken for 3 or 4 units by Stanford grad students.

Client-side technologies used to create web sites such as Google maps or Gmail. Includes HTML5, CSS, JavaScript, the Document Object Model (DOM), and Ajax.