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1 - 10 of 15 results for: CS106A

BIOMEDIN 210: Modeling Biomedical Systems: Ontology, Terminology, Problem Solving (CS 270)

Methods for modeling biomedical systems and for building model-based software systems. Emphasis is on intelligent systems for decision support and Semantic Web applications. Topics: knowledge representation, controlled terminologies, ontologies, reusable problem solvers, and knowledge acquisition. Students learn about current trends in the development of advanced biomedical software systems and acquire hands-on experience with several systems and tools. Prerequisites: CS106A, basic familiarity with biology.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit

CME 151A: Interactive Data Visualization in D3

This four-week short course introduces D3, a powerful tool for creating interactive data visualizations on the web (d3js.org). The class is geared toward scientists and engineers who want to better communicate their personal projects and research through visualizations on the web. The class will cover the basics of D3: inputting data, creating scales and axes, and adding transitions and interactivity, as well as some of the most used libraries: stack, cluster and force layouts. The class will be based on short workshops and a final project. A background in programming methodology at the level of CS106A is assumed. The course will make use of Javascript, experience is recommended but not necessary.
Terms: Spr | Units: 1 | Grading: Satisfactory/No Credit

CME 193: Introduction to Scientific Python

This short course runs for the first four weeks of the quarter. It is recommended for students who are familiar with programming at least at the level of CS106A and want to translate their programming knowledge to Python with the goal of becoming proficient in the scientific computing and data science stack. Lectures will be interactive with a focus on real world applications of scientific computing. Technologies covered include Numpy, SciPy, Pandas, Scikit-learn, and others. Topics will be chosen from Linear Algebra, Optimization, Machine Learning, and Data Science. Prior knowledge of programming will be assumed, and some familiarity with Python is helpful, but not mandatory.
Terms: Aut, Win, Spr | Units: 1 | Grading: Satisfactory/No Credit

CME 250A: Machine Learning on Big Data

A short course presenting the application of machine learning methods to large datasets.Topics include: brief review of the common issues of machine learning, such as, memorizing/overfitting vs learning, test/train splits, feature engineering, domain knowledge, fast/simple/dumb learners vs slow/complex/smart learners; moving your model from your laptop into a production environment using Python (scikit) or R on small data (laptop sized) at first; building math clusters using the open source H2O product to tackle Big Data, and finally to some model building on terabyte sized datasets. Prereqresites: basic knowledge of statistics, matrix algebra, and unix-like operating systems; basic file and text manipulation skills with unix tools: pipes, cut, paste, grep, awk, sed, sort, zip; programming skill at the level of CME211 or CS106A.
Terms: offered occasionally | Units: 1 | Grading: Satisfactory/No Credit

CS 101: Introduction to Computing Principles

Introduces the essential ideas of computing: data representation, algorithms, programming "code", computer hardware, networking, security, and social issues. Students learn how computers work and what they can do through hands-on exercises. In particular, students will see the capabilities and weaknesses of computer systems so they are not mysterious or intimidating. Course features many small programming exercises, although no prior programming experience is assumed or required. CS101 is not a complete programming course such as CS106A. CS101 is effectively an alternative to CS105. A laptop computer is recommended for the in-class exercises.
Terms: Spr | Units: 3-5 | UG Reqs: GER:DB-EngrAppSci | Grading: Letter or Credit/No Credit
Instructors: Parlante, N. (PI)

CS 102: Big Data: Tools and Techniques, Discoveries and Pitfalls

Aimed primarily at students who may not major in CS but want to learn about big data and apply that knowledge in their areas of study. Many of the world's biggest discoveries and decisions in science, technology, business, medicine, politics, and society as a whole, are now being made on the basis of analyzing massive data sets, but it is surprisingly easy to come to false conclusions from data analysis alone, and privacy of data connected to individuals can be a major concern. This course provides a broad introduction to big data: historical context and case studies; privacy issues; data analysis techniques including databases, data mining, and machine learning; sampling and statistical significance; data analysis tools including spreadsheets, SQL, Python, R; data visualization techniques and tools. Tools and techniques are hands-on but at a cursory level, providing a basis for future exploration and application. Prerequisites: high school AP computer science, CS106A, or other equivalent programming experience; comfort with statistics and spreadsheets helpful but not required.
Terms: Spr | Units: 3-4 | UG Reqs: WAY-AQR | Grading: Letter or Credit/No Credit

CS 106A: Programming Methodology (ENGR 70A)

Introduction to the engineering of computer applications emphasizing modern software engineering principles: object-oriented design, decomposition, encapsulation, abstraction, and testing. Uses the Java programming language. Emphasis is on good programming style and the built-in facilities of the Java language. No prior programming experience required. Summer quarter enrollment is limited.
Terms: Aut, Win, Spr, Sum | Units: 3-5 | UG Reqs: GER:DB-EngrAppSci, WAY-FR | Grading: Letter or Credit/No Credit

CS 193C: Client-Side Internet Technologies

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. Prerequisite: programming experience at the level of CS106A.
Terms: Sum | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Young, P. (PI)

CS 270: Modeling Biomedical Systems: Ontology, Terminology, Problem Solving (BIOMEDIN 210)

Methods for modeling biomedical systems and for building model-based software systems. Emphasis is on intelligent systems for decision support and Semantic Web applications. Topics: knowledge representation, controlled terminologies, ontologies, reusable problem solvers, and knowledge acquisition. Students learn about current trends in the development of advanced biomedical software systems and acquire hands-on experience with several systems and tools. Prerequisites: CS106A, basic familiarity with biology.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit

EE 47: Press Play: Interactive Device Design

Introduction to the human-centered and technical workings behind interactive devices ranging from cellphones and video controllers to smart cars and appliances. Students build a working MP3 player prototype of their own design, using embedded microcontrollers, digital audio decoders and component sensors, and other electronic hardware. Topics include electronics prototyping, interface prototyping, sensors and actuators, micro-controller development, physical prototyping, and user testing. Prerequisite: CS106A and X or consent of instructor.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit
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