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11 - 14 of 14 results for: CS107

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

EE 285: Embedded Systems Workshop (CS 241)

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

LINGUIST 180: From Languages to Information (CS 124, 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: CS103, CS107, CS109.
Terms: Win | Units: 3-4 | Grading: Letter or Credit/No Credit

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

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: CS103, CS107, CS109.
Terms: Win | Units: 3-4 | Grading: Letter or Credit/No Credit
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