2019-2020 2020-2021 2021-2022 2022-2023 2023-2024
Browse
by subject...
    Schedule
view...
 

41 - 50 of 129 results for: all courses

CS 145: Data Management and Data Systems

Introduction to the use, design, and implementation of database and data-intensive systems, including data models; schema design; data storage; query processing, query optimization, and cost estimation; concurrency control, transactions, and failure recovery; distributed and parallel execution; semi-structured databases; and data system support for advanced analytics and machine learning. Prerequisites: 103 and 107 (or equivalent).
Terms: Aut | Units: 3-4 | UG Reqs: GER:DB-EngrAppSci

CS 148: Introduction to Computer Graphics and Imaging

This is the introductory prerequisite course in the computer graphics sequence which introduces students to the technical concepts behind creating synthetic computer generated images. The beginning of the course focuses on using Blender to create visual imagery, as well as an understanding of the underlying mathematical concepts including triangles, normals, interpolation, texture mapping, bump mapping, etc. Then we move on to a more fundamental understanding of light and color, as well as how it impacts computer displays and printers. From this we discuss more thoroughly how light interacts with the environment, and we construct engineering models such as the BRDF and discuss various simplifications into more basic lighting and shading models. Finally, we discuss ray tracing technology for creating virtual images, while drawing parallels between ray tracers and real world cameras in order to illustrate various concepts. Anti-aliasing and acceleration structures are also discussed. The more »
This is the introductory prerequisite course in the computer graphics sequence which introduces students to the technical concepts behind creating synthetic computer generated images. The beginning of the course focuses on using Blender to create visual imagery, as well as an understanding of the underlying mathematical concepts including triangles, normals, interpolation, texture mapping, bump mapping, etc. Then we move on to a more fundamental understanding of light and color, as well as how it impacts computer displays and printers. From this we discuss more thoroughly how light interacts with the environment, and we construct engineering models such as the BRDF and discuss various simplifications into more basic lighting and shading models. Finally, we discuss ray tracing technology for creating virtual images, while drawing parallels between ray tracers and real world cameras in order to illustrate various concepts. Anti-aliasing and acceleration structures are also discussed. The final class project consists of building out a ray tracer to create a visually compelling image. Starter codes and code bits will be provided here and there to aid in development, but this class focuses on what you can do with the code as opposed to what the code itself looks like. Therefore grading is weighted towards in person "demos" of the code in action - creativity and the production of impressive visual imagery are highly encouraged.This is the first course in the computer graphics sequence at Stanford. Topics include: Scanline Rendering; Triangles; Rasterization; Transformations; Shading; Triangle Meshes; Subdivision; Marching Cubes; Textures; Light; Color; Cameras; Displays; Tone Mapping; BRDF; Lighting Equation; Global Illumination; Radiosity; Ray Tracing; Acceleration Structures; Sampling; Antialiasing; Reflection; Transmission; Depth of Field; Motion Blur; Monte Carlo; Bidirectional Ray Tracing; Light Maps.
Terms: Aut | Units: 3-4 | UG Reqs: GER:DB-EngrAppSci, WAY-CE

CS 149: Parallel Computing

This course is an introduction to parallelism and parallel programming. Most new computer architectures are parallel; programming these machines requires knowledge of the basic issues of and techniques for writing parallel software. Topics: varieties of parallelism in current hardware (e.g., fast networks, multicore, accelerators such as GPUs, vector instruction sets), importance of locality, implicit vs. explicit parallelism, shared vs. non-shared memory, synchronization mechanisms (locking, atomicity, transactions, barriers), and parallel programming models (threads, data parallel/streaming, MapReduce, Apache Spark, SPMD, message passing, SIMT, transactions, and nested parallelism). Significant parallel programming assignments will be given as homework. The course is open to students who have completed the introductory CS course sequence through 111.
Terms: Aut | Units: 3-4 | UG Reqs: GER:DB-EngrAppSci

CS 154: Introduction to the Theory of Computation

This course provides a mathematical introduction to the following questions: What is computation? Given a computational model, what problems can we hope to solve in principle with this model? Besides those solvable in principle, what problems can we hope to efficiently solve? In many cases we can give completely rigorous answers; in other cases, these questions have become major open problems in computer science and mathematics. By the end of this course, students will be able to classify computational problems in terms of their computational complexity (Is the problem regular? Not regular? Decidable? Recognizable? Neither? Solvable in P? NP-complete? PSPACE-complete?, etc.). Students will gain a deeper appreciation for some of the fundamental issues in computing that are independent of trends of technology, such as the Church-Turing Thesis and the P versus NP problem. Prerequisites: CS 103 or 103B.
Terms: Aut | Units: 3-4 | UG Reqs: GER:DB-EngrAppSci

CS 155: Computer and Network Security

For juniors, seniors, and first-year graduate students. Principles of computer systems security. Attack techniques and how to defend against them. Topics include: network attacks and defenses, operating system security, application security (web, apps, databases), malware, privacy, and security for mobile devices. Course projects focus on building reliable software. Prerequisite: 110. Recommended: basic Unix.
Terms: Spr | Units: 3 | UG Reqs: GER:DB-EngrAppSci

CS 157: Computational Logic

Rigorous introduction to Symbolic Logic from a computational perspective. Encoding information in the form of logical sentences. Reasoning with information in this form. Overview of logic technology and its applications - in mathematics, science, engineering, business, law, and so forth. Topics include the syntax and semantics of Propositional Logic, Relational Logic, and Herbrand Logic, validity, contingency, unsatisfiability, logical equivalence, entailment, consistency, natural deduction (Fitch), mathematical induction, resolution, compactness, soundness, completeness.
Terms: Aut | Units: 3 | UG Reqs: GER:DB-EngrAppSci, WAY-FR

CS 161: Design and Analysis of Algorithms

Worst and average case analysis. Recurrences and asymptotics. Efficient algorithms for sorting, searching, and selection. Data structures: binary search trees, heaps, hash tables. Algorithm design techniques: divide-and-conquer, dynamic programming, greedy algorithms, amortized analysis, randomization. Algorithms for fundamental graph problems: minimum-cost spanning tree, connected components, topological sort, and shortest paths. Possible additional topics: network flow, string searching. Prerequisite: 106B or 106X; 103 or 103B; 109 or STATS 116.
Terms: Aut, Win, Sum | Units: 3-5 | UG Reqs: GER:DB-EngrAppSci, WAY-FR

DESIGN 11: Visual Thinking

Visual Thinking is the foundational class for all designers and creative people at Stanford. It teaches you how to access your creativity through a series of projects. Visual thinking, a powerful adjunct to other problem solving modalities, is developed and exercised in the context of solving some fun and challenging design problems. Along the way, the class expands your access to your imagination, helps you see more clearly with the "mind's eye", and learn how to do rapid visualization and prototyping. The emphasis on basic creativity, learning to build in the 3D and digital world, and fluent and flexible idea production. This class was formerly listed as ME 101, and is a required foundational class for undergrad design majors.
Terms: Aut, Win, Spr | Units: 4 | UG Reqs: GER:DB-EngrAppSci, WAY-CE

DESIGN 141: Product Design Methods

This course will introduce the basic concepts of human factors and demonstrate the importance of understanding and considering human capabilities and limits in product and system design. This will include an overview of both cognitive and physical human characteristics, methods to analyze human factors constraints, and design methods for prototyping and evaluating the usability of physical products and systems. In this course individual- and team-based design projects are used to emphasize the integration between human factors analysis and evaluation, authoring design requirements and translating these to both physical products and systems. Prerequisites: DESIGN 11 (formerly ME101), and DESIGN 121 (formerly ME115A). Strongly recommended: DESIGN 172 (formerly ME110), ME102, Psych 1. This class was formerly listed as ME 115B. It is a required class for undergrad design majors. This class is for design students only.
Terms: Win | Units: 4 | UG Reqs: GER:DB-EngrAppSci
Instructors: Follmer, S. (PI)

EARTHSYS 101: Energy and the Environment (ENERGY 101)

Energy use in modern society and the consequences of current and future energy use patterns. Case studies illustrate resource estimation, engineering analysis of energy systems, and options for managing carbon emissions. Focus is on energy definitions, use patterns, resource estimation, pollution.
Terms: Win | Units: 3 | UG Reqs: GER:DB-EngrAppSci, WAY-AQR, WAY-SMA
Filter Results:
term offered
updating results...
teaching presence
updating results...
number of units
updating results...
time offered
updating results...
days
updating results...
UG Requirements (GERs)
updating results...
component
updating results...
career
updating results...
© Stanford University | Terms of Use | Copyright Complaints