MIT Open Course Schedule

更新

已经连续很多天 “没有” 时间看课程视频了。回过头看，其实从课程视频中获得的信息是十分有限的，现在已经进入了和预期一致的、开始看就会走神的状态，简直和真实的上课状态一模一样。

从一开始的目的来说，我是希望可以通过 “正统” 的视频课程，系统地了解某一门课程的内容，以不至于落后于还在深入学业的同学。但其实，至少对我来说，真正有用的东西大多不是从学校或者课堂学到的。我应该学会不再在乎这样的比较。

有很多不同领域的、有趣的、有价值的视频和内容，都值得被关注。当时间从一开始可以随意浪费，到把时间用来看美剧，到只能把时间用来看课程视频，到连续 1 小时的视频课程视频都没有耐心看完……

开始于 01.11，结束于 05.15。时间有点短了，原准备连续 3 年的。（为什么是 3 年呢 :P

说明

我计划把一些公开课作为学习渠道、有规律地看完，就像列一个课程表一样，而不像其他内容仅仅是消遣，随心所欲地想哪儿就哪儿。我也不知道能坚持多长时间，可能是一天，也可能是一年。因为没有课后作业和考试，时间充足的情况下，应该不会有压力。当然，课程内容不会局限于计算机科学领域。

由于现实环境的不可预测性质，课程表不能规定的太严格，但是公开课又太多了，必须保证足够的进度。初步规则为：

按照 课程数 * 1.5 的方式规划每一门课的结束时间
如果在规定时间内没有完成，就需要在接下来的时间里、下一门课正常进行的情况下，补完上一门课
一门课程结束后，需要在接下来 3 天内选出下一门课
不跳过节假日
存在两门以上课程同时进行的情况
在一门课进行的情况下，如果另一门课结束，不需要补充下一门课。只需要保证至少有一门课在进行

关于多门课同时进行的发现（图）：

最多同时两门课，否则时间来不及
按照 1.5 倍时间的估算，两门课交替一天一次，周末两次，时间刚好没有空挡
确实存在两门以上课程同时进行的情况，时间上不好管理了，只能适当放宽结束时间
（04.15）时间彻底乱了（狗头）

课程表

Title	Intrduction to Computer Science and Programming in Python
No.	6.0001, Fall 2016
Date	2021.01.11 ~ 2021.01.29 （12 lectures）

Lecture 1	What is Computation? 01.11
Lecture 2	Branching and Iteration 01.12
Lecture 3	String Manipulation, Guess and Check, Approximations, Bisection 01.13
Lecture 4	Decomposition, Abstraction, and Function 01.14
Lecture 5	Tuples, Lists, Aliasing, Mutability, and Cloning 01.16
Lecture 6	Recursion and Dictionaries 01.17
Lecture 7	Testing, Debugging, Exceptions, and Assertions 01.20
Lecture 8	Ojbect Oriented Programming 01.23
Lecture 9	Python Classes and Inheritance 01.23
Lecture 10	Understanding Program Efficiency, Part 1 01.24
Lecture 11	Understanding Program Efficiency, Part 2 01.26
Lecture 12	Searching and Sorting 01.29

2 (2.1)

Title	Intrduction to Computational Thinking and Data Science
No.	6.0002, Fall 2016
Date	2021.01.30 ~ 2021.02.23 （15 lectures）

Lecture 1	Introduction, Optimization Problems 01.30
Lecture 2	Optimization Problems 01.31
Lecture 3	Graph-theoretic Models 02.01
Lecture 4	Stochastic Thinking 02.03
Lecture 5	Random Walks 02.05
Lecture 6	Monte Carlo Simulation 02.06
Lecture 7	Confidence Intervals 02.07
Lecture 8	Sampling and Standard Error 02.09
Lecture 9	Understanding Experimental Data 02.10
Lecture 10	Understanding Experimental Data (cont.) 02.12
Lecture 11	Introduction to Machine Learning 02.13
Lecture 12	Clustering 02.14
Lecture 13	Classification 02.15
Lecture 14	Classification and Statistical Sins 02.16
Lecture 15	Statistical Sins and Wrap Up 02.17

3 (2.2)

Title	The Film Experience
No.	MIT 21L011, Fall 2013
Date	2021.01.30 ~ 2021.03.17 （30 lectures）

Lecture 1	Introduction to MIT 21L011 01.30
Lecture 2	Keaton 01.31
Lecture 3	Chaplin, Part I 02.02
Lecture 4	Chaplin, Part II 02.04
Lecture 5	Film as Global & Cultural Form; Montage, Mise en Sciene 02.06
Lecture 6	German Film, Murnau 02.07
Lecture 7	The Studio Era 02.08
Lecture 8	The Work of Movies; Capra & Hawks 02.12
Lecture 9	Alfred Hitchcock 02.13
Lecture 10	Shadow of a Doubt, Reat Window 02.14
Lecture 11	The Musical 02.15
Lecture 12	The Musical (continued) 02.19
Lecture 13	The Western 02.20
Lecture 14	The Western (continued) 02.22
Lecture 15	American Film in the 1970s, Part I 02.25
Lecture 16	American Film in the 1970s, Part II 02.26
Lecture 17	Renoir and Poetic Realism 02.28
Lecture 18	Renoir’s Grand Illusion 03.02
Lecture 19	Italian Neorealism, Part I 03.03
Lecture 20	Italian Neorealism, Part II 03.04
Lecture 21	Truffaut, the Nouvelle Vague, The 400 Blows 03.05
Lecture 22	Kurosawa and Rashomon 03.05
Lecture 23	Summary Perspectives - Film as Art and Artifact 03.06
Record	Meet the Educator 03.07
Record	Why Study Film? 03.07
Record	Approach to Lecturing 03.07
Record	The Film Experience: A Course in Transition 03.07
Record	The Video Lecture Conundrum 03.07
Record	Beyond Film: Television & Literature 03.07
Record	Thematic Spines of the Course 03.07

4 (2.2.2)

Title	Music and Technology
No.	21M.380, Fall 2009
Date	2021.02.20 ~ 2021.02.26 （4 lectures）

Lecture 13	Contemporary History and Aesthetics 02.21
Lecture 12d	Contemporary History and Aesthetics 02.23
Lecture 12w	Contemporary History and Aesthetics 02.23
Lecture 16	Contemporary History and Aesthetics 02.24

5 (5.1)

Title	Introduction to Psychology
No.	MIT 9.00SC, Fall 2011
Date	2021.03.08 ~ 2021.04.13 （24 lectures）

Lecture 1	Introduction to Psychology 03.08
Lecture 2	Introduction to Psychology 03.09
Lecture 3	Introduction to Psychology 03.10
Lecture 4	Introduction to Psychology 03.11
Lecture 5	Introduction to Psychology 03.12
Lecture 6	Introduction to Psychology 03.14
Lecture 7	Introduction to Psychology 03.14
Lecture 8	Introduction to Psychology 03.16
Lecture 9	Introduction to Psychology 03.18
Lecture 10	Introduction to Psychology 03.25
Lecture 11	Introduction to Psychology 03.28
Lecture 12	Introduction to Psychology 03.30
Lecture 13	Introduction to Psychology 04.05
Lecture 14	Introduction to Psychology 04.12
Lecture 15	Introduction to Psychology 04.18
Lecture 16	Introduction to Psychology 04.25
Lecture 17	Introduction to Psychology 04.29
Lecture 18	Introduction to Psychology 05.06
Lecture 19	Introduction to Psychology –
Lecture 20	Introduction to Psychology 05.16
Lecture 21
Lecture 22
Lecture 23
Lecture 24

6 (5.2)

Title	Artificial Intelligence
No.	MIT6.034, Fall 2010
Date	2021.03.12 ~ 2021.04.27 （30 lectures）

Lecture 1	Intruduction and Scope 03.12
Lecture 2	Reasonging Goal Trees and Problem Solving 03.14
Lecture 3	Based Expert Systems 03.20
Lecture 4	Search: Depth-First, Hill Climbing, Bean 03.20
Lecture 5	Search: Optional, Branch and Bound, A* 03.28
Lecture 6	Search Games, Minimax, and Alpha-Beta 03.31
Lecture 7	Constraints: Interpreting Line Drawings 04.07
Lecture 8	Constraints: Search, Domain Reduction 04.13
Lecture 9	Constraints: Vistual Object Recognition 04.18
Lecture 10	Introduction to Learning, Nearest Neighbors 04.19
Lecture 11	Learning: Identification Trees, Disorder 04.21
Lecture 12a	Neural Nets 04.27
Lecture 12b	Deep Neural Nets 05.03
Lecture 13
Lecture 14
Lecture 15
Lecture 16
Lecture 17
Lecture 18
Lecture 19
Lecture 20
Lecture 21
Lecture 22
Lecture 23
Lecture 24
Lecture 25
Lecture 26
Lecture 27
Lecture 28
Lecture 29
Lecture 30

7 (5.3)

Title	Design and Analysis of Algorithms
No.	MIT6.046J, Fall 2015
Date	2021.03.14 ~ 2021.05.03 （30 lectures）

Lecture 1	Clourse Overview, Interval Scheduling 03.14
Lecture 2	Divide & Conquer: Convex Hull, Median Finding 03.15
Lecture R1	Matrix Multiplication and the Master Theoren 03.15
Lecture 3	Divide & Conquer: FFT 03.17
Lecture R2	2-3 Trees and B-Trees 03.20
Lecture 4	Divide & Conquer: van Emde Boas Trees 03.27
Lecture 5	Amortization: Amortized Analysis 04.01
Lecture 6	Randomization: Matrix Multiply, Quicksort 04.08
Lecture 9
Lecture 10
Lecture 11
Lecture 12
Lecture 13
Lecture 14
Lecture 15
Lecture 16
Lecture 17
Lecture 18
Lecture 19
Lecture 20
Lecture 21
Lecture 22
Lecture 23
Lecture 24
Lecture 25
Lecture 26
Lecture 27
Lecture 28
Lecture 29
Lecture 30
Lecture 31
Lecture 32
Lecture 33
Lecture 34

8 (5.4)

Title	Mathematics for Computer Science
No.	MIT6.042J, Spring 2015
Date	2021.03.16 ~ 2021.08.31 （111 lectures）

Lecture 1.1.1	Welcome to 6.042 03.16
Lecture 1.1.2	Intro to Proofs: Part 1 03.15
Lecture 1.1.3	Intro to Proofs: Part 2 03.17
Lecture 1.2.1	Proof by Contradiction 03.17
Lecture 1.2.3	Proof by Cases 03.17
Lecture 1.3.1	Well Ordering Principle 1 03.17
Lecture 1.3.3	Well Ordering Principle 2 03.18
Lecture 1.3.5	Well Ordering Principle 3 –
Lecture 1.4.1	Propositional Operators 03.19
Lecture 1.4.3	Digital Logic 03.19
Lecture 1.4.4	Truth Tables –
Lecture 1.5.1	Predicate Logic 1 –
Lecture 1.5.2	Predicate Logic 2 03.25
Lecture 1.5.4	Predicate Logic 3 –
Lecture 1.6.1	Sets Definitions 03.30
Lecture 1.7.1	Relations 03.31
Lecture 1.7.3	Relational Mappings 04.07
Lecture 1.7.5	Finite Cardinality 04.07
Lecture 1.8.1	Induction 04.10
Lecture 1.8.2	Bogus Induction 04.12
Lecture 1.8.4	Strong Induction 04.13
Lecture 1.8.6	WOP vs Induction 04.15
Lecture 1.9.1	State Machines Invariants 04.16
Lecture 1.9.3	Derived Variables 04.17
Lecture 1.10.1	Recursive Data 04.18
Lecture 1.10.4	Structural Induction 04.19
Lecture 1.10.7	Recursive Function 04.19
Lecture 1.11.1	Cardinality 04.19
Lecture 1.11.3	ountable Sets 04.21
Lecture 1.11.4	Cantor’s Theorem 04.24
Lecture 1.11.7	The Halting Problem 04.25
Lecture 1.11.9	Russell’s Paradox 04.25
Lecture 1.11.11	Set Theory Axioms 04.27
Lecture 2.1.1	GCDs & Linear Combinations 04.27
Lecture 2.1.2	Euclidean Algorithms 04.28
Lecture 2.1.4	Pulverizer 04.29
Lecture 2.1.6	Revisiting Die Hard 04.30
Lecture 2.1.7	Prime Factorization 05.02
Lecture 2.2.1	Congruence mod n 05.04
Lecture 2.2.3	Inverses mod n 05.09
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9 (6)

Title	Introduction to Deep Learning
No.	MIT6.S191
Date	2021.04.15 ~ 2021.06.31 （40 lectures）

Lecture 1	Introduction to Deep Learning 04.18
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Lecture
Lecture
Lecture
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试听后放弃的课程

2021

MIT 15.S12 Blockchain and Money, Fall 2018 01.24
MIT 3.091 Introduction to Solid-State Chemistry, Fall 2018 01.24
MIT MAS.S62 Cryptocurrency Engineering and Design, Spring 2018 03.08