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1 - 10 of 10 results for: CS106A

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: Spr | 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

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

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

IMMUNOL 207: Essential Methods in Computational and Systems Immunology

Introduction to the major underpinnings of systems immunology: first principles of development of computational approaches to immunological questions and research; details of the algorithms and statistical principles underlying commonly used tools; aspects of study design and analysis of data sets. Prerequisites: CS106a and CS161 strongly recommended.
Terms: Spr | Units: 3 | Grading: Medical Option (Med-Ltr-CR/NC)

ME 52SI: Scan, Model, Print! Designing with 3D Technology

Think 3D scanning, modeling, and printing technology is just about plastic widgets? Think again! Immerse yourself in a world of custom prosthetics, manufacturing in space, autonomous cars, and much more. This hands-on engineering design course teaches advanced 3D imaging and computational modeling skills in order to leverage the unique benefits of additive manufacturing to solve complex problems. Students will connect the theory behind these tools to direct experience with the equipment and software. Short assignments at the start of the quarter will build students' core competencies and prepare them for a team-based, open-ended project. Class time will be a mixture of lecture, lab, guest speakers, and field trips. Recommended: basic CAD, fabrication, and programming experience (e.g. ME103D, 203, CS106A or equivalents).
Terms: not given this year | Units: 2 | Grading: Satisfactory/No Credit

ME 116M: Introduction to the Design of Smart Products

This course will focus on the technical mechatronic skills as well as the human factors and interaction design considerations required for the design of smart products and devices. Students will learn techniques for rapid prototyping of smart devices, best practices for physical interaction design, fundamentals of affordances and signifiers, and interaction across networked devices. Students will be introduced to design guidelines for integrating electrical components such as PCBs into mechanical assemblies and consider the physical form of devices, not just as enclosures but also as a central component of the smart product. Prerequisites include: CS106A, E40, and ME 210, or instructor approval.
Terms: Spr | Units: 4 | Grading: Letter or Credit/No Credit

PHYSICS 91SI: Practical Computing for Scientists

Essential computing skills for researchers in the natural sciences. Helping students transition their computing skills from a classroom to a research environment. Topics include the Unix operating system, the Python programming language, and essential tools for data analysis, simulation, and optimization. More advanced topics as time allows. Prerequisite: CS106A or equivalent.
Terms: Spr | Units: 2 | Grading: Satisfactory/No Credit
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