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CS246 | Home - Stanford University

The course will discuss data mining and machine learning algorithms for analyzing very large amounts of data. The emphasis will be on MapReduce and Spark as tools for creating parallel algorithms that can process very large amounts of data.

Actived: Friday Apr 16, 2021

Detail: http://web.stanford.edu/class/cs246/

CS 276: Information Retrieval and Web Search

Posted: (0 seconds ago) In this course, we will cover basic and advanced techniques for building text-based information systems, including the following topics: Efficient text indexing; Boolean and vector-space retrieval models; Evaluation and interface issues; IR techniques for the web, …

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CS142: Web Applications

Posted: (0 seconds ago) CS142: Web Applications (Spring 2021) Course Description. Although the World-Wide Web was initially conceived as a vehicle for delivering documents, it is now being used as a platform for sophisticated interactive applications, displacing the traditional mechanism of installable binaries.

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CS253 - Web Security

Posted: (0 seconds ago) This course is a comprehensive overview of web security. The goal is to build an understanding of the most common web attacks and their countermeasures. Given the pervasive insecurity of the modern web landscape, there is a pressing need for programmers and system designers to improve their understanding of web security issues. We'll be covering the fundamentals as well as the state-of-the-art in web security. Topics include: Principles of web security, attacks and countermeasures, the browser sec…

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CS101 Syllabus - Stanford University

Posted: (0 seconds ago) Your overall course grade will be determined as a weighted average of the following categories: Homework 40% (5% each) Paper 10% Midterm 20% Final 30% Homeworks. We'll have weekly homeworks, eight in total. The exam problems will very much resemble the homework problems, so the point of the homeworks is giving you practice on problems so you ...

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CS 520: Knowledge Graphs - Stanford University

Posted: (0 seconds ago) Course Info . Knowledge graphs have emerged as a compelling abstraction for organizing world's structured knowledge over the internet, capturing relationships among key entities of interest to enterprises, and a way to integrate information extracted from multiple data sources.

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Stanford CS 224N | Natural Language Processing with Deep ...

Posted: (0 seconds ago) Natural language processing (NLP) is a crucial part of artificial intelligence (AI), modeling how people share information. In recent years, deep learning approaches have obtained very high performance on many NLP tasks. In this course, students gain a thorough introduction to cutting-edge neural networks for …

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CS 140: Operating Systems - Stanford University

Posted: (0 seconds ago) The course divides into three major sections. The first part of the course discusses concurrency: how to manage multiple tasks that execute at the same time and share resources. Topics in this section include processes and threads, context switching, synchronization, scheduling, and deadlock.

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CS244: Advanced Topics in Networking, Spring 2020

Posted: (0 seconds ago) CS244 is a graduate course in computer networks. In this class we'll explore the principles and design decisions which underly the Internet. We'll explore the pros and cons of the current design, and give some thought to how we can make the Internet better in future.

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CS349D Cloud Computing Technology, Autumn 2018

Posted: (0 seconds ago) Class Format:You will need to fill out a Google form with answers to a few summary questions before each class starts. The form will be emailed to students each week.During class, one or two students will spend 10-15 minutes presenting the day's paper, and will then lead the subsequentdiscussion. Another student will take notes on the presentation and discussion. Class Presentations/Notes Google Folder:If you are assigned to take notes for a class, please take the notes in a Google Doc and add them to this …

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CS 148: Introduction to Computer Graphics and Imaging

Posted: (0 seconds ago) 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 ...

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CME 193 - Introduction to Scientific Python

Posted: (0 seconds ago) This short course runs for the first eight weeks of the quarter and isoffered each quarter during the academic year.It is recommended for students who want to use Python in math, science,or engineering courses and for students who want to learn the basics ofPython programming, and learn about relevant applications. The goal of the short course is to familiarize students with Python’stools for scientific computing.Lectures will be interactive with a focus on learning by example, andassignments will be app…

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CS231A: Computer Vision, From 3D Reconstruction to Recognition

Posted: (0 seconds ago) What is the best way to reach the course staff? Stanford students please use an internal class forum on Piazza so that other students may benefit from your questions and our answers. If you have a personal matter, email us at the class instructors mailing list ([email protected]) .

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CS124 - From Languages to Information (Winter 2021)

Posted: (0 seconds ago) Taking the course as a sophomore is recommended, but we also get lots of juniors and a reasonable number of frosh; the course is designed to be taken early in your Stanford career. It will help if you have at least done some programming beyond 106B, and is also useful to have had 107, but not required.

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CS 101 - Intro to Computers - Stanford University

Posted: (0 seconds ago) Course Policies: HW. Weekly homework, out Tuesday/Thursday, due following Wednesday Combination of written questions and code exercises Two free 24-hour late days (pre-approved extensions) Each late day covers 1 second to 24 hours late Submit on Canvas Short response paper on Artificial Intelligence

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CS230 Deep Learning - Stanford University

Posted: (0 seconds ago) CS230 has the following components: 1. In class lecture - once a week (hosted on Zoom). You can access lectures by going to the “Zoom” tab of Canvas. 2. Video lectures, programming assignments, and quizzes on Coursera 3. The final project 4. Weekly TA-led sections

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ENGR110/210: Perspectives in Assistive Technology - Course ...

Posted: (0 seconds ago) Course Description: Perspectives in Assistive Technology is a one-quarter (10-week) course taught at Stanford during the Winter Quarter that explores the design, development, and use of technology that benefits people with disabilities and older adults. Students from diverse disciplines (mostly mechanical engineers) and from all academic years ...

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CS 97SI: Introduction to Programming Contests

Posted: (0 seconds ago) Instructor: Jaehyun Park(Stanford ACM-ICPC coach)Subscribe to the Stanford ACM-ICPC email listto get notifications about future practice contests.(Added on 8/21/2013) This class was taught in 2011-12 Winter. I'm getting a lot of emails asking if I'm teaching it again, but there is no plan to offer the course at the moment.(Added on 6/30/2015) All the slides are rewritten in LaTeX now.

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EE269 - Signal Processing for Machine Learning

Posted: (0 seconds ago) Course description. This course will introduce you to fundamental signal processing concepts and tools needed to apply machine learning to discrete signals. You will learn about commonly used techniques for capturing, processing, manipulating, learning and classifying signals. The topics include: mathematical models for discrete-time signals ...

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CS122: Artificial Intelligence - Philosophy, Ethics, and ...

Posted: (0 seconds ago) Course Summary . Recent advances in computing may place us at the threshold of a unique turning point in human history. Soon we are likely to entrust management of our environment, economy, security, infrastructure, food production, healthcare, and to a large degree even our personal activities, to artificially intelligent computer systems.

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CS224S: Spoken Language Processing - Stanford University

Posted: (0 seconds ago) Course Information. This course is designed around lectures, assignments, and a course project to give students practical experience building spoken language systems. We will use modern software tools and algorithmic approaches. There are no exams. We aim for each student to build something they are proud of. Homework topics:

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CS 102: Working with Data - Tools and Techniques

Posted: (0 seconds ago) A note from Prof. Jennifer Widom, June 2020: This was the last offering of CS 102. Congratulations to the students who were able to persevere through a pandemic and horrific racism to complete the course and gain some mastery of working with data, and a big thanks to …

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EE364a: Convex Optimization I - Stanford University

Posted: (0 seconds ago) The intro lecture video is on Canvas, under Panopto Course Video tab. John made a mistake recording the first live lecture (there is no recording), but it was an administrative lecture and all information should be available on the course website. We have a discussion board set up on Piazza.

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CS227:Knowledge Representation and Reasoning, Spr2011

Posted: (0 seconds ago) The course work will consist of assignments a mideterm and a final exam. While portions of the assignments will be conceptual, the project-oriented section of the assignment will require implementation work using a specific knowledge representation and reasoning system.

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CS 193A: Android Application Development

Posted: (0 seconds ago) Course Description: This course provides an introduction to developing applications for the Android mobile platform. Prerequisite: CS 106B or equivalent. Java experience recommended. Devices: Access to an Android phone and/or tablet recommended but not required. See Links page for some cheap tablet recommendations)

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CS224W | Home - Stanford University

Posted: (0 seconds ago) This course focuses on the computational, algorithmic, and modeling challenges specific to the analysis of massive graphs. By means of studying the underlying graph structure and its features, students are introduced to machine learning techniques and data mining tools apt to reveal insights on a …

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Statistical Learning and Data Mining

Posted: (0 seconds ago) This course is the fifth in a series, and follows our popular past offerings: Modern Regression and Classification (1996-2000) This new two-day course gives a detailed and modern overview of statistical models used by data scientists for prediction and inference.

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World History of Science - Course Syllabus

Posted: (0 seconds ago) Course Goals: The goals of winter/spring IHUM courses are the following: to introduce students in a sustained way to a body of material in a specific discipline; to hone the reading, analytical, and critical thinking skills begun in fall quarter; and to continue to develop …

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Syllabus — stats60 1.0 documentation

Posted: (0 seconds ago) Course description¶. By the end of the course, students should be able to: Enter tabular data using R.; Plot data using R, to help in exploratory data analysis.; Formulate regression models for the data, while understanding some of the limitations and assumptions implicit in using these models.

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MSandE 234 | Data Privacy and Ethics - Stanford University

Posted: (0 seconds ago) The course evaluation consists of three parts: problem sets (40%), in-class discussion leading and participation (20%), and group project reports and presentations (40%). Students will rotate to lead Wednesday discussions. There will be 3 problem sets that …

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Course Syllabus - Stanford University

Posted: (0 seconds ago) Course Syllabus . Course Description: This class is designed to help students whose native language is not English to speak clearly and effectively as graduate students in academic settings. During the class students will: · Be analyzed to identify individual speech characteristics and …

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CS107 Computer Organization & Systems - Stanford University

Posted: (0 seconds ago) Course Logistics. Lectures: Mon & Fri 1:00PM-2:20PM PDT via Zoom (link on Canvas) Labs: Tue/Wed/Thu at various times on Zoom; students sign up for labs after the quarter begins. Mid-Quarter Assessment Assessment Window: Wed May 5 12:00PM PT through Fri May 7 12:00PM PT The assessment is open-book, timed, and will be taken through BlueBook, an electronic test-taking …

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CS 148: Introduction to Computer Graphics and Imaging

Posted: (0 seconds ago) Course Outline. Content and slides for this course were borrowed from Pat Hanrahan's CS 148 and CS 348B classes, Marc Levoy's computational and digital photography classes, Bernd Girod's EE classes, Michael Lentine and Jon Su's CS 248 class, and James O' Brien and Ravi Ramamoorthi's classes at …

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Syllabus for FS101 - Stanford University

Posted: (0 seconds ago) The purpose of this course is to introduce students to the interdisciplinary field of feminist scholarship, which seeks to understand the creation and perpetuation of gender inequalities. After tracing the historical emergence of feminist critiques, the course surveys contemporary feminist issues, particularly work and family, health and ...

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Stanford University: Tensorflow for Deep Learning Research

Posted: (0 seconds ago) This course will cover the fundamentals and contemporary usage of the Tensorflow library for deep learning research. We aim to help students understand the graphical computational model of TensorFlow, explore the functions it has to offer, and learn how to build …

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Game Theory - Stanford University

Posted: (0 seconds ago) The course will provide the basics: representing games and strategies, the extensive form (which computer scientists call game trees), Bayesian games (modeling things like auctions), repeated and stochastic games, and more. We'll include a variety of examples including …

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CS106B: Programming Abstractions in C++

Posted: (0 seconds ago) In the meantime, feel free to check out the course information handout and syllabus to learn more about what this class is all about, the prerequisites, and the course policies. If you have any questions in the meantime, feel free to email me at [email protected] with questions.

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CS109: Probability for Computer Scientists, Spring 2021

Posted: (0 seconds ago) Want to meet with an experienced peer to discuss course concepts, think through a problem set, or prepare for an upcoming exam? CTL offers appointment tutoring for CS 109, in addition to tutoring for a number of other courses. For more information and to schedule an appointment, visit our tutoring appointments and drop-in schedule page.

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EFS 698B: Syllabus

Posted: (0 seconds ago) Jun 16, 2013  · Course content. The course will have two parts. In class we will focus attention on various points of academic writing form and style, both in general and as they relate to your particular field. There will also be individual meetings (typically a twenty minute meeting each week) during which time we will work on areas specific to your own ...

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STATS 32 Fall 2018/2019 - Stanford University

Posted: (0 seconds ago) Course Description. This short course runs for weeks two through five of the quarter. It is recommended for undergraduate students who want to use R in the humanities or social sciences and for students who want to learn the basics of R programming. The goal of the short course is to familiarize students with R's tools for data analysis.

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EE368/CS232: Digital Image Processing -- Class Information

Posted: (0 seconds ago) Course Description Image sampling and quantization, color, point operations, segmentation, morphological image processing, linear image filtering and correlation, image transforms, eigenimages, multiresolution image processing, noise reduction and restoration, feature extraction and recognition tasks, image registration.

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Graduate courses in Computer Science

Posted: (0 seconds ago) Student teams under faculty supervision work on research and implementation of a large project in AI. State-of-the-art methods related to the problem domain. Prerequisites: AI course from 220 series, and consent of instructor. 3 units, Aut (Koller, D), Win (Ng, A) CS 294H. Social Software

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