Обучаемся самостоятельно: подборка видеокурсов по Computer Science

Содержание

1. Введение в Computer Science
2. Структуры данных и Алгоритмы
3. Системное программирование
4. Распределенные системы
5. Базы данных
6. Объектно-ориентированный дизайн и разработка софта
7. Искусственный интеллект
8. Машинное обучение
9. Веб-разработка и интернет-технологии
10. Concurrency
11. Компьютерные сети
12. Разработка мобильных приложений
13. Математика для программистов
14. Теория информатики и языки программирования
15. Архитектура компьютера
16. Безопасность
17. Компьютерная графика
18. Работа с изображениями и компьютерное зрение
19. Интерфейс Человек-Компьютер
20. Вычислительная биология
21. Прочее

Поддержка публикации — компания Edison, которая тестирует критические системы на отказоустойчивость, а так же проектирует и разрабатывает ПО для кластерных вычислений.

Введение в Computer Science

• 6.00SC — Introduction to Computer Science and Programming (весна 2011) — MIT OCW
• 6.00 — Introduction to Computer Science and Programming (осень 2008) — MIT OCW
• 6.01SC — Introduction to Electrical Engineering and Computer Science I — MIT OCW
• 6.001 — Structure and Interpretation of Computer Programs, MIT (Textbook)
• CS 10 The Beauty & Joy of Computing, весна 2015 — UCBerkeley
• CS 50 — Introduction to Computer Science, Harvard University (cs50.tv)
• CS 61A — Structure and Interpretation of Computer Programs [Python], UC Berkeley
• SPD1 — Systematic Program Design [Racket], University of British Columbia
• CS 101 — Computer Science 101, Stanford University (Register free to access Videos)
• CS E-1 Understanding Computers and the Internet, Spring 2013 — Harvard Extension School (Весна 2011)
• CSE 142 Computer Programming I (C Programming), Autumn 200 — University of Washington
• CS1301 Intro to computing — Gatech
• CS 106A — Programming Methodology, Stanford University
• CS 106B — Programming Abstractions, Stanford University
• CS 107 — Programming Paradigms, Stanford University

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Структуры данных и Алгоритмы

• CS 61B — Data Structures, UC Berkeley
• MOOC — Design and Analysis of Algorithms Part 1 — Prof Roughgarden — Coursera (Part 2)
• MOOC — Algorithms Part 1 — Prof Sedgewick (Part 2)
• COP 3530 Data Structures and Algorithms, Prof Sahni, UFL (Videos)
• CS2: Data Structures and Algorithms — Richard Buckland — UNSW
• 6.006 — Introduction to Algorithms, MIT OCW
• CS 161 — Design and Analysis of Algorithms, Prof. Tim Roughgarden, Stanford University
• CSE 373 — Analysis of Algorithms, Stony Brook — Prof Skiena
• CS16 Introduction to Algorithms and Data Structures — Brown University
• 6.046J — Introduction to Algorithms — Fall 2005, MIT OCW
• 6.046 — Design and Analysis of Algorithms, Весна 2015 — MIT OCW
• CS 473: Algorithms — University of Illinois at Urbana-Champaign
• Programming Challenges — Prof Skiena
• 16s-4102 — Algorithms, University of Virginia (Youtube)
• CS 170 Algorithms — Весна 2015 — UCBerkeley
• COP 5536 Advanced Data Structures, Prof Sahni — UFL (Videos)
• CS 261 — A Second Course in Algorithms, Stanford University (Lectures) (Youtube)
• CS 224 — Advanced Algorithms, Harvard University (Lecture Videos) (Youtube)
• ECS 122A — Algorithm Design and Analysis, UC Davis
• CSEP 521: Applied Algorithms, Winter 2013 — University of Washington (Videos)
• CS 6150 — Advanced Algorithms (Fall 2016), University of Utah
• ECS 222A — Graduate Level Algorithm Design and Analysis, UC Davis
• 6.851 — Advanced Data Structures, MIT (MIT OCW)
• 6.854 — Advanced Algorithms, MIT (Prof. Karger lectures)
• CS264 Beyond Worst-Case Analysis, Fall 2014 — Tim Roughgarden Lecture (Youtube)
• CS364A Algorithmic Game Theory, Fall 2013 — Tim Roughgarden Lectures
• CS364B Advanced Mechanism Design, Winter 2014 — Tim Roughgarden Lectures
• Algorithms — Aduni
• Advanced Topics in Algorithms and Datastructures — SS 2005 — Universität Freiburg
• Algorithmentheorie/Algorithms Theory — WS 2013 — Universität Freiburg (WS 2011)
• Theory I — SS 2010 — Universität Freiburg
• CS225 — Data Structures — University of Illinois at Urbana-Champaign

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Системное программирование

• 6.033 Computer System Engineering — MIT
• CS24 Introduction to Computing Systems — California Institute of Technology (Весна 15 version)
• 15-213 Introduction to Computer Systems, Fall 2015 — CMU
• CS361 — COMPUTER SYSTEMS — UIC
• CS124 Operating Systems — California Institute of Technology
• Systems — Aduni
• CS 162 — Operating Systems and Systems Programming, UC Berkeley (Lectures — YouTube)
• CS 4414 — Operating Systems, University of Virginia
• CSE 421/521 — Introduction to Operating Systems, SUNY University at Buffalo, NY — Весна 2016 (Lectures — YouTube)
• CS 377 Fall 16: Operating Systems — Umass OS
• 6.828: Operating System Engineering [Fall 2014]
• CSEP 551 Operating Systems Autumn 2014 — University of Washington
• CS194 Advanced Operating Systems Structures and Implementation, Весна 2013, UC Berkeley
• CPCS 663 — Real-Time Systems: Video Material — TAMU
• CS 251: Intermediate Software Design (C++ version)
• CS 251 (2015): Intermediate Software Design
• CSE 30341 — Spr 2008: Operating Systems
• CSE 60641 — Fall 08: Graduate Operating Systems
• 6.172 Performance Engineering of Software Systems — MIT OCW
• Software Engineering for Self Adaptive Systems — iTunes — HPI
• Real-Time Systems — SS 2013 — Universität Freiburg
• System Infrastructure For Data Science — WS 2012 — Universität Freiburg

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Распределенные системы

• VU:Distributed Systems: Principles and Paradigms by Maarten van Steen (Fall 2012), Vrije Universiteit, Amsterdam
• CS 677 Весна 16: Distributed Operating Systems — Umass OS
• CS 436: Distributed Computer Systems — U Waterloo
• 6.824: Distributed Systems, Весна 2015 — MIT
• Distributed Algorithms, https://canvas.instructure.com/courses/902299
• CS138 Distributed Computer Systems Весна 2016 — Brown University
• CSEP 552: PMP Distributed Systems, Весна 2013 — University of Washington (Videos)
• CSE 490H: Scalable Systems: Design, Implementation and Use of Large Scale Clusters, Autumn 2008 — University of Washington (Videos)
• MOOC — Cloud Computing Concepts — UIUC

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Базы данных

• CS121 — Introduction to Relational Database Systems, Fall 2016 — Caltech
• CS122 — Relational Database System Implementation, Winter 2014-2015 — Caltech
• CS 5530 — Database Systems, Весна 2016, University of Utah
• MOOC — Database Stanford Dbclass
• CSEP 544, Database Management Systems, Au 2015 — University of Washington
• CMPSC 431W Database Management Systems, Fall 2015 — PSU
• Principles of Database Management, Bart Baesens
• 15-721 — Database Systems, CMU (Lectures — YouTube)
• CS 186 — Database Systems, UC Berkeley, Весна 2015 (Lectures- YouTube)
• CS 6530 — Graduate-level Database Systems, Fall 2016, University of Utah (Lectures — YouTube)
• 6.830/6.814: Database Systems [Fall 2014]
• FIT9003 Database Systems Design, Rob Meredith, Monash University

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Объектно-ориентированный дизайн и разработка софта

• ECE 462 Object-Oriented Programming using C++ and Java — Purdue
• Object-oriented Program Design and Software Engineering — Aduni
• Object Oriented Systems Analysis and Design (Systems Analysis and Design in a Changing World)
• Computer Science 169- Software Engineering — Весна 2015 — UCBerkeley
• Introduction to Service Design and Engineering — University of Trento, Italy
• OOSE: Software Dev Using UML and Java
• CS 411 — Software Architecture Design, Bilkent University
• CS 164 Software Engineering — Harvard
• Model Driven Archtitecture — WS 2005 — Universität Freiburg
• Software Design, Modelling and Analysis in UML — WS 2012 — Universität Freiburg

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Искусственный интеллект

• CS 188 — Introduction to Artificial Intelligence, UC Berkeley
• 6.034 Artificial Intelligence, MIT OCW
• 15-780: Graduate Artificial Intelligence, Весна 14, CMU
• CSE 592 Applications of Artificial Intelligence, Winter 2003 — University of Washington
• Advanced AI Techniques — WS 2005 — Universität Freiburg (WS 2004)

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Машинное обучение

Introduction to Machine Learning

• MOOC Machine Learning Andrew Ng — Coursera/Stanford (Notes)
• MOOC — Statistical Learning, Stanford University
• CS 156 — Learning from Data, Caltech
• 10-601 — Introduction to Machine Learning (MS), Carnegie Mellon University
• 10-701 — Introduction to Machine Learning (PhD) — Tom Mitchell, Spring 2011, Carnegie Mellon University (Fall 2014)
• Microsoft Research — Machine Learning Course
• CS 446 — Machine Learning, Fall 2016, UIUC(Fall 2015 Lectures)
• undergraduate machine learning at UBC 2012, Nando de Freitas
• CS 229 — Machine Learning — Stanford University
• CS 189/289A Introduction to Machine Learning, Prof Jonathan Shewchuk — UCBerkeley
• CS 5350/6350 — Machine Learning, Fall 2016, University of Utah
• ECE 5984 Introduction to Machine Learning, Spring 2015 — Virginia Tech
• STA 4273H (Winter 2015): Large Scale Machine Learning
• CS 485/685 Machine Learning, Shai Ben-David, University of Waterloo
• Machine Learning and Data Mining — WS 2004 — Universität Freiburg

Data Mining

• CSEP 546, Data Mining — Pedro Domingos, Sp 2016 — University of Washington (YouTube)
• CS 5140/6140 — Data Mining, Spring 2016, University of Utah (Youtube)
• CS 5955/6955 — Data Mining, University of Utah (YouTube)
• Statistical Aspects of Data Mining (Stats 202) — Google
• MOOC — Text Mining and Analytics by ChengXiang Zhai
• Information Retrieval SS 2014, iTunes — HPI
• MOOC — Data Mining with Weka
• CS 290 DataMining Lectures
• CS246 — Mining Massive Data Sets, Winter 2016, Stanford University (YouTube)

Probabilistic Graphical Modeling

• CS 6190 — Probabilistic Modeling, Spring 2016, University of Utah
• 10-708 — Probabilistic Graphical Models, Carnegie Mellon University
• Probabilistic Graphical Models, Daphne Koller, Stanford University

Deep Learning

• Deep learning at Oxford 2015 — Nando de Freitas
• DS-GA 1008 — Deep Learning, New York University
• Deep Learning, Stanford University
• Deep Learning — University of Waterloo

Advanced Machine Learning

• Machine Learning 2013 — Nando de Freitas, UBC
• Machine Learning: 2014-2015, University of Oxford
• 10-702/36-702 — Statistical Machine Learning — Larry Wasserman, Spring 2016, CMU (Spring 2015)
• 10-715 Advanced Introduction to Machine Learning — CMU (YouTube)

Natural Language Processing and Computer Vision

• CS 224d — Deep Learning for Natural Language Processing, Stanford University (Lectures — Youtube)
• CS 224N — Natural Language Processing, Stanford University
• MOOC: Natural Language Processing, Dan Jurafsky & Chris Manning — Coursera
• MOOC — Natural Language Processing — Coursera, University of Michigan
• CS 231n — Convolutional Neural Networks for Visual Recognition, Stanford University
• Machine Learning for Computer Vision — TUM

Misc Machine Learning Topics

• CS 6955 — Clustering, Spring 2015, University of Utah
• Info 290 — Analyzing Big Data with Twitter, UC Berkeley school of information
• 10-725 Convex Optimization: Spring 2015 — CMU
• 10-801 Advanced Optimization and Randomized Algorithms
• CS 229r — Algorithms for Big Data, Harvard University (Youtube)
• CAM 383M — Statistical and Discrete Methods for Scientific Computing, University of Texas
• Statistical Learning- Classification — University of Waterloo
• 9.520 — Statistical Learning Theory and Applications, Fall 2015 — MIT
• Reinforcement Learning — UCL

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Concurrency

• CSE P 506 – Concurrency (Весна 2011) University of Washington (Videos)
• CSEP 524 — Parallel Computation — University of Washington (Videos)
• CS 282 (2014): Concurrent Java Network Programming in Android
• Concurrency Theory and Practice — WS 2010 — Universität Freiburg

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Компьютерные сети

• Prof. Shiv Kalyanaraman's Online Audio and Video Lectures on Computer Networking
• Audio/Video Recordings and Podcasts of Professor Raj Jain's Lectures — Washington University in St. Louis (YouTube)
• Computer Networks, Tanenbaum, Wetherall Computer Networks 5e — Video Lectures (U Washington MOOC)
• CSEP 561: PMP Network Systems, Fall 2013 — University of Washington (Videos)
• CSEP 561 – Network Systems, Autumn 2008 — University of Washington (Videos)
• Introduction to Data Communications 2013, Steven Gordon — Thammasat University, Thailand
• Communication Systems — SS 2008 — Universität Freiburg
• Communication Systems (Telecommunication from ISDN/GSM to VoIP) — WS 2010 — Universität Freiburg
• Internetworking — SS 2005 — Universität Freiburg
• Mobile Computing — WS 2004 — Universität Freiburg
• Network Algorithms — SS 2013 — Universität Freiburg
• Telecommunication Systems — SS 2012 — Universität Freiburg
• Wireless Sensor Networks — WS 2006 (English) — Universität Freiburg

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Разработка мобильных приложений

• MOOC Programming Mobile Applications for Android Handheld Systems — University of Maryland
• CS 193p — Developing Applications for iOS, Stanford University
• CS S-76 Building Mobile Applications — Harvard
• CSSE490 Android Development Rose-Hulman Winter 2010-2011, Dave Fisher
• iOS Course, Dave Fisher

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Математика для программистов

• 6.042J — Mathematics for Computer Science, Fall 2010, MIT OCW
• 6.042J — Mathematics for Computer Science, Весна 15, MIT OCW
• Computer Science 70, 001 — Fall 2012
• 6.041 Probabilistic Systems Analysis and Applied Probability — MIT OCW
• 10-600 Math Background for ML — CMU
• Linear Algebra Review — CMU
• Statistics 110: Probability
• 18.06 — Linear Algebra, Prof. Gilbert Strang, MIT OCW
• 36-705 — Intermediate Statistics — Larry Wasserman, CMU
• STATS 250 — Introduction to Statistics and Data Analysis, UMichigan
• 131B — Introduction to Probability and Statistics, UCI
• Multiple View Geometry — Lecture 1 (Prof. Daniel Cremers) TUM
• The Probability and Statistics Full Course — YouTube
• A first course in Linear Algebra — N J Wildberger — UNSW

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Веб-разработка и интернет-технологии

• CS 75 Building Dynamic Websites — Harvard University
• Web Search — SS 2006 — Universität Freiburg
• CSEP545: Transaction Processing for E-Commerce, Winter 2012 — University of Washington (Videos)
• CT 310 Web Development — Colorado State University
• Internet Technologies and Applications 2012, Steven Gordon — Thammasat University, Thailand
• CSCI 3110 Advanced Topics in Web Development, Fall 2011 — ETSU iTunes
• CSCI 5710 e-Commerce Implementation, Fall 2015 — ETSU iTunes
• XML and Semantic Web-Technologies — SS 2005 — Universität Freiburg

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Теория информатики и языки программирования

• MOOC — Compilers — Stanford University
• CS 164 Hack your language, UC Berkeley (Lectures — Youtube)
• CS 173 Programming Languages, Brown University (Book)
• CS 421 — Programming Languages and Compilers, UIUC (Videos)
• CSC 253 — CPython internals: A ten-hour codewalk through the Python interpreter source code, University of Rochester
• CSEP 501 — Compiler Construction, University of Washington (Lectures — Youtube)
• CSEP 505 Programming Languages, Winter 2015 — University of Washington
• DMFP — Discrete Mathematics and Functional Programming, Wheaton College
• CS 374 — Algorithms & Models of Computation (Fall 2014), UIUC (Lecture videos)
• 6.045 Automata, Computability, and Complexity, MIT (Lecture Videos)
• CS581 Theory of Computation — Portland State University (Lectures — Youtube)
• Theory of Computation — Fall 2011 UC Davis
• TDA555 Introduction to Functional Programming — Chalmers University of Technology (Lectures — YouTube)
• Philip Wadler Haskell lecture recordings
• Functional Programming — University of Edinburgh — 2016-17
• MOOC — Functional Programming Principles in Scala by Martin Odersky (YouTube)

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Архитектура компьютера

• How Computers Work — Aduni
• 6.004 — Computation Structures Весна 2013, MIT
• CS 61C — Machine Structures, UC Berkeley (Lectures — YouTube)
• CS1: Higher Computing — Richard Buckland UNSW
• 18-447 — Introduction to Computer Architecture, CMU (Lectures — YouTube — Fall 15)
• CS 152 Computer Architecture and Engineering, UC Berkeley
• CSEP 548 — Computer Architecture Autumn 2012 — University of Washington
• 15-418 — Parallel Computer Architecture and Programming, CMU (Lecture Videos)
• EE445L Embedded Systems Design Lab, Fall 2015, UTexas
• CS149 Embedded Systems — Fall 2014 — UCBerkeley
• ECE 4760 Designing with Microcontrollers Fall 2016, Cornell University (Lectures — Youtube)
• CS 267 Applications of Parallel Computers, Весна 16 — UC Berkeley (YouTube)
• CMPE 118/L(218/L) — Mechatronics — Fall 2015
• Software Engineering for Embedded Systems — WS 2010/11 — iTunes — HPI
• ELEC2141 Digital Circuit Design, UNSW

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Безопасность

• 6.858 Computer Systems Security — MIT OCW
• CSEP590A: Practical Aspects of Modern Cryptography, Winter 2011 — University of Washington (Videos)
• CIS 4930/ CIS 5930 — Offensive Computer Security, Florida State University
• 18-636 Browser Security, Stanford
• Internet Security — Weaknesses and Targets (WT 2015/16) (WT 2012/13 (YouTube))
• IT Security, Steven Gordon — Thammasat University, Thailand
• Security and Cryptography, Steven Gordon — Thammasat University, Thailand
• Web Security — SS 2008 — Universität Freiburg
• CS461/ECE422 — Computer Security — University of Illinois at Urbana-Champaign (Videos)

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Компьютерная графика

• CS 5630/6630 — Visualization, Fall 2016, University of Utah (Lectures — Youtube)
• Advanced Visualization UC Davis
• Computer Graphics Fall 2011
• Introduction to Graphics Architecture
• CS184 Computer Graphics, Fall 2012 — UC Berkeley

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Обработка изображений и компьютерное зрение

• EE225B Digital Image Processing, Весна 2014 — UC Berkeley (Videos — Весна 2006)
• EE637: Digital Image Processing I — Purdue University (Videos — Sp 2011 ,Videos — Sp 2007)
• Image Processing and Analysis UC Davis
• CAP 5415 — Computer Vision, University of Central Florida
• Lecture: Variational Methods for Computer Vision (Prof. D. Cremers) TUM
• Image Processing and Analysis (Course) UC Davis
• Introduction to Vision and Robotics
• EENG 512 / CSCI 512 — Computer Vision — Colorado School of Mines

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Интерфейс Человек-Компьютер

• CS147: Introduction to Human-Computer Interaction Design — Stanford
• CSEP 510: Human Computer Interaction
• Programming for Designers — COMP1400-T2 (2010) — UNSW

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Вычислительная биология

• Skiena's Computational Biology Lectures
• 6.802J/ 6.874J Foundations of Computational and Systems Biology — MIT OCW
• Bioinformatic II — WS 2010 — Universität Freiburg

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Прочее

• Skiena's Computational Finance Lectures
• AM 207 — Monte Carlo Methods and Stochastic Optimization, Harvard University
• CS 223A — Introduction to Robotics, Stanford University
• CS 3152 — Introduction to Computer Game Development, Cornell University
• Open Sourced Elective: Database and Rails — Intro to Ruby on Rails, University of Texas (Lectures — Youtube)
• SCICOMP — An Introduction to Efficient Scientific Computation, Universität Bremen (Lectures — Youtube)
• MIT CMS.611J Creating Video Games, Fall 2014
• Lecture: Visual Navigation for Flying Robots — TUM
• CS E-259 XML with Java, Java Servlet, and JSP — Harvard
• CSE 40373 — Spr 2009: Multimedia Systems
• Exposing Digital Photography — Harvard Extension School
• XML and Databases — WS 2011 — Universität Freiburg

Автор: Edison

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