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MIT计算机科学及编程导论

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2015-08-10 00:43:46

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/

计算机科学

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计算机科学及编程导论

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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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