printer friendly pageCS 91: Digital Canvas: An Introduction to UI/UX Design
This course is focused on the application of UX/UI design concepts to actual user interfaces: the creation of wireframes, high-fidelity mockups, and clickable prototypes. We will be focusing on what makes a good or bad user interface, effective design techniques, and how to employ these techniques using Figma and Marvel to make realistic prototypes. This course is ideal for anyone with little to no visual design experience who would like to build their skill set in UI/UX for app or web design. It would also be ideal for anyone with experience in front or back-end web development or human-computer interaction that would want to sharpen their visual design and analysis skills for UI/UX.
CS 91SI: Digital Canvas: An Introduction to UI/UX Design
In this course, students learn digital design in a low-stress environment. We will teach the essential concepts of UI/UX design and create actual user interfaces in a project-based format. By the end of the class, students will have experience in creating handoff-ready interactive high-fidelity mockups for a realistic product feature. This course covers what makes a good or bad interface, effective design techniques from the ground up, and how to execute on design principles using the tool Figma. Limited enrollment - admission determined by short application due 11:59 PM on March 23:
https://forms.gle/knsLbRwt7th4HHsb7 . No required prerequisites. Recommended: some prior experience in product design, human-computer interaction, or front-end engineering
Last offered: Spring 2023
CS 93: Teaching AI
For graduate students who are TA-ing an AI course. This course prepares new AI section leaders to teach, write, and evaluate AI content. In class, you will be evaluating final projects individually and as a group. You will have discussions criticizing papers and assigning grades to them. You will analyze and solve discussion session problems on the board, explain algorithmsnlike backpropagation, and learn how to give constructive feedback to students. The class will also include a guest speaker who will give teaching advice and talk about AI. Focus is on teaching skills, techniques, and final projects grading. The class meets once a week for the first 6 weeks of the quarter.
Last offered: Autumn 2019
CS 100ACE: Problem-solving Lab for CS106A
Additional problem solving practice for the introductory CS course
CS 106A. 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 106A required.
Terms: Aut, Win, Spr
| Units: 1
Instructors:
King, E. (PI)
CS 100BACE: Problem-solving Lab for CS106B
Additional problem solving practice for the introductory CS course
CS106B. 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 106B required.
Terms: Aut, Win, Spr
| Units: 1
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.
Last offered: Autumn 2018
| UG Reqs: GER:DB-EngrAppSci, WAY-FR
CS 102: Working with Data - Tools and Techniques
Aimed at non-CS undergraduate and graduate students who want to learn a variety of tools and techniques for working with data. 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 data sets. This course provides a broad and practical introduction to working with data: data analysis techniques including databases, data mining, machine learning, and data visualization; data analysis tools including spreadsheets, Tableau, relational databases and SQL, Python, and R; introduction to network analysis and unstructured data. Tools and techniques are hands-on but at a cursory level, providing a basis for future exploration and application. Prerequisites: comfort with basic logic and mathematical concepts, along with high school AP computer science,
CS106A, or other equivalent programming experience.
Last offered: Spring 2020
| UG Reqs: WAY-AQR
CS 103: Mathematical Foundations of Computing
What are the theoretical limits of computing power? What problems can be solved with computers? Which ones cannot? And how can we reason about the answers to these questions with mathematical certainty? This course explores the answers to these questions and serves as an introduction to discrete mathematics, computability theory, and complexity theory. At the completion of the course, students will feel comfortable writing mathematical proofs, reasoning about discrete structures, reading and writing statements in first-order logic, and working with mathematical models of computing devices. Throughout the course, students will gain exposure to some of the most exciting mathematical and philosophical ideas of the late nineteenth and twentieth centuries. Specific topics covered include formal mathematical proofwriting, propositional and first-order logic, set theory, binary relations, functions (injections, surjections, and bijections), cardinality, basic graph theory, the pigeonhole prin
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What are the theoretical limits of computing power? What problems can be solved with computers? Which ones cannot? And how can we reason about the answers to these questions with mathematical certainty? This course explores the answers to these questions and serves as an introduction to discrete mathematics, computability theory, and complexity theory. At the completion of the course, students will feel comfortable writing mathematical proofs, reasoning about discrete structures, reading and writing statements in first-order logic, and working with mathematical models of computing devices. Throughout the course, students will gain exposure to some of the most exciting mathematical and philosophical ideas of the late nineteenth and twentieth centuries. Specific topics covered include formal mathematical proofwriting, propositional and first-order logic, set theory, binary relations, functions (injections, surjections, and bijections), cardinality, basic graph theory, the pigeonhole principle, mathematical induction, finite automata, regular expressions, the Myhill-Nerode theorem, context-free grammars, Turing machines, decidable and recognizable languages, self-reference and undecidability, verifiers, and the P versus NP question. Students with significant proofwriting experience are encouraged to instead take
CS154. Students interested in extra practice and support with the course are encouraged to concurrently enroll in
CS103A. Prerequisite: CS106B or equivalent. CS106B may be taken concurrently with
CS103.
Terms: Aut, Win, Spr, Sum
| Units: 3-5
| UG Reqs: GER:DB-Math, WAY-FR
Instructors:
Aiken, A. (PI)
;
Lian, Z. (PI)
;
Liu, A. (PI)
;
Schwarz, K. (PI)
;
Agashe, R. (TA)
;
Aiu, K. (TA)
;
Anvari, M. (TA)
;
Cao, S. (TA)
;
Carrell, T. (TA)
;
Chang, T. (TA)
;
Chung, J. (TA)
;
Han, R. (TA)
;
Jee, Y. (TA)
;
Kung, B. (TA)
;
Pandya, D. (TA)
;
Raman, V. (TA)
;
Rodriguez, J. (TA)
;
Roman, E. (TA)
;
Saue- Fletcher, L. (TA)
;
Sotoudeh, M. (TA)
;
Xia, W. (TA)
;
Zhai, W. (TA)
CS 103ACE: Mathematical Problem-solving Strategies
Problem solving strategies and techniques in discrete mathematics and computer science. Additional problem solving practice for
CS103. In-class participation required. Prerequisite: consent of instructor. Co-requisite:
CS103.
Terms: Aut, Win, Spr
| Units: 1
Instructors:
Guan, R. (PI)
CS 104: Introduction to Essential Software Systems and Tools
Concepts that are prerequisites to many different CS classes, such as version control, debugging, and basic cryptography and networking, are either left for students to figure out on their own or are taught in "crash course" form on-the-fly during other, unrelated classes. We propose to develop a course that will teach students the skills necessary to be successful computer scientists, such as the command line, source code management and debugging, security and cryptography, containers and virtual machines, and cloud computing. In this course, students will both become proficient with practical tools and develop a deeper, intuitive understanding of the involved software systems and computer science concepts. With this deeper understanding, students can leverage critical thinking skills to intelligently and efficiently configure and troubleshoot software systems, assess the security and efficiency of particular tool usages, and synthesize new automation pipelines that integrate multiple
more »
Concepts that are prerequisites to many different CS classes, such as version control, debugging, and basic cryptography and networking, are either left for students to figure out on their own or are taught in "crash course" form on-the-fly during other, unrelated classes. We propose to develop a course that will teach students the skills necessary to be successful computer scientists, such as the command line, source code management and debugging, security and cryptography, containers and virtual machines, and cloud computing. In this course, students will both become proficient with practical tools and develop a deeper, intuitive understanding of the involved software systems and computer science concepts. With this deeper understanding, students can leverage critical thinking skills to intelligently and efficiently configure and troubleshoot software systems, assess the security and efficiency of particular tool usages, and synthesize new automation pipelines that integrate multiple tools. To summarize, instead of having just a cursory understanding of how to use these tools, students will learn how to most effectively use these tools to become proficient programmers and computer scientists. In addition, this course can provide a gentle introduction to potentially challenging computer science concepts (e.g., networking) that become a focus in subsequent courses and also help motivate some of the tool usages they will see later in the degree program.
Terms: Win
| Units: 3
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