Online образование — различия между версиями

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=== Khan Academy ===
=== Khan Academy ===
==== Subjects ====
Very HUGE playlist with:
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*Test preparing
*Talks and Interviews
== Audience ==
== Audience ==

Текущая версия на 22:02, 28 апреля 2017

Кафедра ТМ > Интересные ссылки > Online образование



Online lectures have some advantages over the traditional in-person instruction:

  • they allow students to control the pacing of a lecture – they can speed it up or instantly replay the material;
  • a large library of online classes could allow students to personalize their education, students could combine many different lecture chunks to create courses tailored to their interests and abilities;
  • analytical programs built into the course-hosting system could allow faculty to monitor a course in real time, tracking student progress and adjusting their teaching techniques to maximize effectiveness throughout the quarter.
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Online education[править]



Computer Science[править]



Civil Engineering[править]

Electrical Engr.[править]

Complex Systems[править]

Online lectures[править]

Academic Earth[править]


  • Art & Architecture
  • Astronomy
  • Biology
  • Business
  • Chemistry
  • Computer Science
  • Economics
  • Education
  • Electrical Engineering
  • Engineering (Except Electrical)
  • Entrepreneurship
  • Environmental Studies
  • History
  • International Relations
  • Law
  • Literature
  • Mathematics
  • Media Studies
  • Medicine & Healthcare
  • Online Bachelor's Degrees
  • Online Courses for Credit
  • Online Master's Degrees
  • Online Professional Certificates
  • Philosophy
  • Physics
  • Political Science
  • Psychology
  • Religious Studies
  • Test Preparation
  • Writing


  • Berkeley
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  • Harvard
  • Khan Academy
  • Maryland
  • Michigan
  • MIT
  • Norwich
  • NYU
  • Princeton
  • Stanford
  • UCLA
  • UNSW
  • USC
  • Yale


YouTube channel of Stanford

Stanford Engineering Everywhere (SEE) programming includes one of Stanford’s most popular engineering sequences: the three-course Introduction to Computer Science taken by the majority of Stanford undergraduates, and seven more advanced courses in artificial intelligence and electrical engineering.


Introduction to Computer Science[править]
  • Programming Methodology CS106A
  • Programming Abstractions CS106B
  • Programming Paradigms CS107
Artificial Intelligence[править]
  • Introduction to Robotics CS223A
  • Natural Language Processing CS224N
  • Machine Learning CS229
Linear Systems and Optimization[править]
  • The Fourier Transform and its Applications EE261
  • Introduction to Linear Dynamical Systems EE263
  • Convex Optimization I EE364A
  • Convex Optimization II EE364B
Additional School of Engineering Courses[править]

Khan Academy[править]

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Group 20510/1
Audience Course
Веренинов Игорь Machine Learning
Dainis Dzenushko Machine Learning
Kovalev Oleg Design and Analysis of Algorithms I
Краморов Данил CS101
Пшенов Антон Design and Analysis of Algorithms I
Симонов Роман Machine Learning
Степанов Алексей Design and Analysis of Algorithms I
Фролова Ксения Game Theory