MIT计算机科学及编程导论

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2015-08-10 00:43:46
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课程号:6.00SC 课程名:Introduction to Computer Science and Programming (Spring 2011) 1-26P英文字幕版 27-52P无字幕版 http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/
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Lecture 1: Introduction to 6.00
41:27
Lecture 2: Core Elements of a Program
49:48
Lecture 3: Problem Solving
47:55
Lecture 4: Machine Interpretation of a Program
50:17
Lecture 5: Objects in Python
50:58
Lecture 6: Recursion
49:23
Lecture 7: Debugging
49:57
Lecture 8: Efficiency and Order of Growth
50:09
Lecture 9: Memory and Search Methods
48:07
Lecture 10: Hashing and Classes
44:56
Lecture 11: OOP and Inheritance
49:36
Lecture 12: Introduction to Simulation and Random Walks
50:23
Lecture 13: Some Basic Probability and Plotting Data
42:48
Lecture 14: Sampling and Monte Carlo Simulation
50:51
Lecture 15: Statistical Thinking
51:30
Lecture 16: Using Randomness to Solve Non-random Problems
49:43
Lecture 17: Curve Fitting
50:59
Lecture 18: Optimization Problems and Algorithms
49:41
Lecture 19: More Optimization and Clustering
49:42
Lecture 20: More Clustering
49:08
Lecture 21: Using Graphs to Model Problems, Part 1
50:50
Lecture 22: Using Graphs to Model Problems, Part 2
48:53
Lecture 23: Dynamic Programming
53:40
Lecture 24: Avoiding Statistical Fallacies
49:35
Lecture 25: Queuing Network Models
52:38
Lecture 26: What Do Computer Scientists Do?
50:04
Lecture 1: Introduction to 6.00
41:27
Lecture 2: Core Elements of a Program
49:48
Lecture 3: Problem Solving
47:55
Lecture 4: Machine Interpretation of a Program
50:17
Lecture 5: Objects in Python
50:58
Lecture 6: Recursion
49:23
Lecture 7: Debugging
49:57
Lecture 8: Efficiency and Order of Growth
50:09
Lecture 9: Memory and Search Methods
48:06
Lecture 10: Hashing and Classes
44:56
Lecture 11: OOP and Inheritance
49:35
Lecture 12: Introduction to Simulation and Random Walks
50:23
Lecture 13: Some Basic Probability and Plotting Data
42:48
Lecture 14: Sampling and Monte Carlo Simulation
50:51
Lecture 15: Statistical Thinking
51:30
Lecture 16: Using Randomness to Solve Non-random Problems
49:43
Lecture 17: Curve Fitting
50:59
Lecture 18: Optimization Problems and Algorithms
49:41
Lecture 19: More Optimization and Clustering
49:42
Lecture 20: More Clustering
49:08
Lecture 21: Using Graphs to Model Problems, Part 1
50:50
Lecture 22: Using Graphs to Model Problems, Part 2
48:53
Lecture 23: Dynamic Programming
53:39
Lecture 24: Avoiding Statistical Fallacies
49:35
Lecture 25: Queuing Network Models
52:38
Lecture 26: What Do Computer Scientists Do?
50:04
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