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1 - 10 of 13 results for: CS107

CS 107: Computer Organization and Systems

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.
Terms: Aut, Win, Spr | Units: 3-5 | UG Reqs: GER:DB-EngrAppSci, WAY-FR | Grading: Letter or Credit/No Credit

CS 107E: Computer Systems from the Ground Up

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. Prerequisite: 106B or X, and consent of instructor. There is a $50 required lab fee.
Terms: Win, Spr | Units: 3-5 | UG Reqs: WAY-FR | Grading: Letter or Credit/No Credit

CS 124: From Languages to Information (LINGUIST 180, LINGUIST 280)

Extracting meaning, information, and structure from human language text, speech, web pages, social networks. Methods include: string algorithms,edit distance, language modeling, the noisy channel, naive Bayes, inverted indices, collaborative filtering, PageRank. Applications such as question answering, sentiment analysis, information retrieval, text classification, social network models, chatbots, sequence alignment, spell checking, speech processing, recommender systems. nPrerequisites:nCS103 explorecourses.stanford.edu/search?view=catalog&filter-coursestatus-Active=on&page=0&q= CS103>,nCS107 explorecourses.stanford.edu/search?view=catalog&filter-coursestatus-Active=on&page=0&q= CS107>,nCS109 explorecourses.stanford.edu/search?view=catalog&filter-coursestatus-Active=on&page=0&q= CS109>
Terms: Win | Units: 3-4 | Grading: Letter or Credit/No Credit
Instructors: Jurafsky, D. (PI)

CS 166: Data Structures

Techniques in the design, analysis, and implementation of data structures. Isometries between data structures (including red/black trees and 2-3-4 trees), amortized analysis (including Fibonacci heaps and splay trees), and randomization (including count-min sketches and dynamic perfect hash tables). Data structures for integers and strings (including van Emde Boas trees and suffix trees). Possible additional topics include functional data structures, concurrent data structures, and spatial data structures. Prerequisites: CS107 and CS161.
Terms: Spr | Units: 3-4 | Grading: Letter or Credit/No Credit
Instructors: Schwarz, K. (PI)

CS 168: The Modern Algorithmic Toolbox

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.
Terms: Spr | Units: 3-4 | Grading: Letter or Credit/No Credit

CS 173: A Computational Tour of the Human Genome

(Only one of 173 or 273A counts toward any CS degree program.) Introduction to computational biology through an informatic exploration of the human genome. Topics include: genome sequencing; functional landscape of the human genome (genes, gene regulation, repeats, RNA genes, epigenetics); genome evolution (comparative genomics, ultraconservation, co-option). Additional topics may include population genetics, personalized genomics, and ancient DNA. Course includes primers on molecular biology, the UCSC Genome Browser, and text processing languages. Guest lectures on current genomic research topics. Class will be similar in spirit to CS273A, which will not be offered this year. Prerequisites: CS107 or equivalent background in programming.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

CS 193P: iOS Application Development

Tools and APIs required to build applications for the iPhone and iPad platforms using the iOS SDK. User interface design for mobile devices and unique user interactions using multi-touch technologies. Object-oriented design using model-view-controller paradigm, memory management, Swift programming language. Other topics include: object-oriented database API, animation, multi-threading, networking and performance considerations. nPrerequisites: C language and object-oriented programming experience exceeding 106B or X level. Previous completion of any one of the following is required: CS 107 explorecourses.stanford.edu/search?view=catalog&filter-coursestatus-Active=on&page=0&q= CS107>, 108 (preferred) or 110. Recommended: UNIX, graphics, databases.
Terms: Aut, Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Hegarty, P. (PI)

CS 241: Embedded Systems Workshop (EE 285)

Project-centric building hardware and software for embedded computing systems. Students work on an existing project of their own or join one of these projects. Syllabus topics will be determined by the needs of the enrolled students and projects. Examples of topics include: interrupts and concurrent programming, deterministic timing and synchronization, state-based programming models, filters, frequency response, and high-frequency signals, low power operation, system and PCB design, security, and networked communication. Prerequisite: CS107 (or equivalent).
Terms: Aut | Units: 2 | Repeatable for credit | Grading: Letter or Credit/No Credit

CS 246: Mining Massive Data Sets

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. The focus is on algorithms and systems for mining big data. nTopics include: Big data systems (Hadoop, Spark, Hive); Link Analysis (PageRank, spam detection, hubs-and-authorities); Similarity search (locality-sensitive hashing, shingling, minhashing, random hyperplanes); Stream data processing; Analysis of social-network graphs; Association rules; Dimensionality reduction (UV, SVD, and CUR decompositions); Algorithms for very-large-scale mining (clustering, nearest-neighbor search); Large-scale machine learning (gradient descent, support-vector machines, classification, and regression); Submodular function optimization; Computational advertising. Prerequisites: At least one of CS107 or CS145.
Terms: Win | Units: 3-4 | Grading: Letter or Credit/No Credit

EE 180: Digital Systems Architecture

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: CS107 (required) and EE108 (recommended but not required).
Terms: Win | Units: 4 | UG Reqs: GER:DB-EngrAppSci, WAY-SMA | Grading: Letter or Credit/No Credit
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