Results for CS107

This course covers the fundamentals of functional programming and algebraic type systems, and explores a selection of related programming paradigms and current research. Haskell is taught and used throughout the course, though much of the material is applicable to other languages. Material will be covered from both theoretical and practical points of view, and topics will include higher order functions, immutable data structures, algebraic data types, type inference, lenses and optics, effect systems, concurrency and parallelism, and dependent types. Prerequisites: Programming maturity and comfort with math proofs, at the levels of CS107 and CS103.

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. Prerequisites: 106B or X, or consent of instructor.

Additional problem solving practice for the introductory CS course CS107. Sections are designed to allow students to acquire a deeper understanding of CS and its applications, work collaboratively, and develop a mastery of the material. Limited enrollment, permission of instructor required. Concurrent enrollment in CS 107 required.

Introduction to the fundamental concepts of computer systems through bare metal programming on the Raspberry Pi. Explores how five concepts come together in computer systems: hardware, architecture, assembly code, the C language, and software development tools. Students do all programming with a Raspberry Pi kit and several add-ons (LEDs, buttons). Topics covered include: the C programming language, data representation, machine-level code, computer arithmetic, compilation, memory organization and management, debugging, hardware, and I/O. Enrollment limited to 40. Check website for details: http://cs107e.stanford.edu on student selection process. Prerequisite: CS106B or CS106X, and consent of instructor. There is a $75 course lab fee.

Explores operating system concepts including concurrency, synchronization, scheduling, processes, virtual memory, I/O, file systems, and protection. Available as a substitute for CS110 that fulfills any requirement satisfied by CS110. Prerequisite: CS107.

Extracting meaning, information, and structure from human language text, speech, web pages, social networks. Introducing methods (regex, edit distance, naive Bayes, logistic regression, neural embeddings, inverted indices, collaborative filtering, PageRank), applications (chatbots, sentiment analysis, information retrieval, question answering, text classification, social networks, recommender systems), and ethical issues in both. Prerequisites: CS106B, Python (at the level of CS106A), CS109 (or equivalent background in probability), and programming maturity and knowledge of UNIX equivalent to CS107 (or taking CS107 or CS1U concurrently).

This course will provide a rigorous and hands-on introduction to the central ideas and algorithms that constitute the core of the modern algorithms toolkit. Emphasis will be on understanding the high-level theoretical intuitions and principles underlying the algorithms we discuss, as well as developing a concrete understanding of when and how to implement and apply the algorithms. The course will be structured as a sequence of one-week investigations; each week will introduce one algorithmic idea, and discuss the motivation, theoretical underpinning, and practical applications of that algorithmic idea. Each topic will be accompanied by a mini-project in which students will be guided through a practical application of the ideas of the week. Topics include hashing, dimension reduction and LSH, boosting, linear programming, gradient descent, sampling and estimation, and an introduction to spectral techniques. Prerequisites: CS107 and CS161, or permission from the instructor.

Build mobile applications using tools and APIs in iOS. Developing applications for the iPhone and iPad requires integration of numerous concepts including functional programming, object-oriented programming, computer-human interfaces, graphics, animation, reactive interfaces, Model-View-Intent (MVI) and Model-View-View-Model (MVVM) design paradigms, object-oriented databases, networking, and interactive performance considerations including multi-threading. This course will require you to learn a new programming language (Swift) as well as the iOS development environment, SwiftUI. Prerequisites: All coursework (homework and final project) involves writing code, so writing a lot of code should not be new to you (coding experience in almost any language is valuable, but object-oriented (e.g. CS108) and/or functional programming languages (e.g. CS43) are most highly recommended).  CS106A and B (or X) and CS107 (or equivalent) are hard prerequisites. Any other courses that help to develop your maturity as a programmer are also recommended.

Hands-on game development in C++ using Unreal Engine 4, the game engine that triple-A games like Fortnite, PUBG, and Gears of War are all built on. Students will be introduced to the Unreal editor, game frameworks, physics, AI, multiplayer and networking, UI, and profiling and optimization. Project-based course where you build your own games and gain a solid foundation in Unreal's architecture that will apply to any future game projects. Pre-requisites: CS106B or CS106X required. CS107 and CS110 recommended.

Project-centric building hardware and software for embedded computing systems. This year the course projects are on a large interactive light sculpture to be installed in Packard. Syllabus topics will be determined by the needs of the enrolled students and projects. Examples of topics include: interrupts and concurrent programming, mechanical control, state-based programming models, signaling and frequency response, mechanical design, power budgets, software, firmware, and PCB design. Interested students can help lead community workshops to begin building the installation. Prerequisites: one of CS107, EE101A, EE108, ME80.

The availability of massive datasets is revolutionizing science and industry. This course discusses data mining and machine learning algorithms for analyzing very large amounts of data. Topics include: Big data systems (Hadoop, Spark); Link Analysis (PageRank, spam detection); Similarity search (locality-sensitive hashing, shingling, min-hashing); Stream data processing; Recommender Systems; Analysis of social-network graphs; Association rules; Dimensionality reduction (UV, SVD, and CUR decompositions); Algorithms for large-scale mining (clustering, nearest-neighbor search); Large-scale machine learning (decision tree ensembles); Multi-armed bandit; Computational advertising. Prerequisites: At least one of CS107 or CS145.

This course provides a comprehensive introduction to computer graphics, focusing on fundamental concepts and techniques in Computer Animation and Physics Simulation. Topics include numerical integration, 3D character modeling, keyframe animation, skinning/rigging, inverse kinematics, rigid body dynamics, deformable body simulation, and fluid simulation. Prerequisites: CS107 and MATH51.

Research seminar on software techniques for emerging computational substrates with guest lectures from hardware designers from research and industry. This seminar explores the benefits of novel hardware technologies, the challenges gating broad adoption of these technologies, and how software techniques can help mitigate these challenges and improve the usability of these hardware platforms. Note that the computational substrates discussed vary depending on the semester. Topics covered include: In-memory computing platforms, dynamical system-solving mixed-signal devices, exible and bendable electronics, neuromorphic computers, intermittent computing platforms, ReRAMs, DNA-based storage, and optical computing platforms. Prerequisites: CS107 or CS107E (required) and EE180 (recommended).

The design of processor-based digital systems. Instruction sets, addressing modes, data types. Assembly language programming, low-level data structures, introduction to operating systems and compilers. Processor microarchitecture, microprogramming, pipelining. Memory systems and caches. Input/output, interrupts, buses and DMA. System design implementation alternatives, software/hardware tradeoffs. Labs involve the design of processor subsystems and processor-based embedded systems. Formerly EE 108B. Prerequisite: one of CS107 or CS 107E (required) and EE108 (recommended but not required).

EE 185A/B/C is a full-year sequence that teaches all of the concepts, knowledge, skills, and techniques to engineer all aspects of a smart object. This second course focuses on understanding the art of specification by writing a specification and fabricating to someone else is written specification. We will also explore embedded system design and the impact of design decisions by redesigning the electronics from EE 185A to meet low power specifications. Students will learn about power, energy, micro controllers, low-level software and how, in embedded systems, electronic hardware, mechanical design, and software are coupled. Course prerequisites: EE 185A as well as CS107, CS107E or instructor approval.

Project-centric building hardware and software for embedded computing systems. This year the course projects are on a large interactive light sculpture to be installed in Packard. Syllabus topics will be determined by the needs of the enrolled students and projects. Examples of topics include: interrupts and concurrent programming, mechanical control, state-based programming models, signaling and frequency response, mechanical design, power budgets, software, firmware, and PCB design. Interested students can help lead community workshops to begin building the installation. Prerequisites: one of CS107, EE101A, EE108, ME80.

Research seminar on software techniques for emerging computational substrates with guest lectures from hardware designers from research and industry. This seminar explores the benefits of novel hardware technologies, the challenges gating broad adoption of these technologies, and how software techniques can help mitigate these challenges and improve the usability of these hardware platforms. Note that the computational substrates discussed vary depending on the semester. Topics covered include: In-memory computing platforms, dynamical system-solving mixed-signal devices, exible and bendable electronics, neuromorphic computers, intermittent computing platforms, ReRAMs, DNA-based storage, and optical computing platforms. Prerequisites: CS107 or CS107E (required) and EE180 (recommended).

Extracting meaning, information, and structure from human language text, speech, web pages, social networks. Introducing methods (regex, edit distance, naive Bayes, logistic regression, neural embeddings, inverted indices, collaborative filtering, PageRank), applications (chatbots, sentiment analysis, information retrieval, question answering, text classification, social networks, recommender systems), and ethical issues in both. Prerequisites: CS106B, Python (at the level of CS106A), CS109 (or equivalent background in probability), and programming maturity and knowledge of UNIX equivalent to CS107 (or taking CS107 or CS1U concurrently).

Extracting meaning, information, and structure from human language text, speech, web pages, social networks. Introducing methods (regex, edit distance, naive Bayes, logistic regression, neural embeddings, inverted indices, collaborative filtering, PageRank), applications (chatbots, sentiment analysis, information retrieval, question answering, text classification, social networks, recommender systems), and ethical issues in both. Prerequisites: CS106B, Python (at the level of CS106A), CS109 (or equivalent background in probability), and programming maturity and knowledge of UNIX equivalent to CS107 (or taking CS107 or CS1U concurrently).