# playlist: click the video's upper-left icon
source: MIT OpenCourseWare 2017年12月3日
MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016
View the complete course: http://ocw.mit.edu/6-0002F16
Instructor: John Guttag
This course provides students with an understanding of the role computation can play in solving problems. Student will learn to write small programs using the Python 3.5 programming language. More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu
1 40:57 Introduction and Optimization Problems
2 48:04 Optimization Problems
3 50:11 Graph-theoretic Models
4 49:50 Stochastic Thinking
5 49:21 Random Walks
6 50:05 Monte Carlo Simulation
7 50:29 Confidence Intervals
8 46:45 Sampling and Standard Error
9 47:06 Understanding Experimental Data
10 50:33 Understanding Experimental Data (cont.)
11 51:31 Introduction to Machine Learning
12 50:40 Clustering
13 49:54 Classification
14 49:25 Classification and Statistical Sins
15 44:43 Statistical Sins and Wrap Up
Showing posts with label A. (subjects)-Engineering & Physical Sciences-Computer Science & Programming-Computational Thinking and Data Science. Show all posts
Showing posts with label A. (subjects)-Engineering & Physical Sciences-Computer Science & Programming-Computational Thinking and Data Science. Show all posts
2017-06-02
Introduction to Computational Thinking and Data Science (Fall 2016) by John Guttag at MIT
# click the upper-left icon to select videos from the playlist
source: MIT OpenCourseWare 2017年5月19日
MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016
This course provides students with an understanding of the role computation can play in solving problems. Student will learn to write small programs using the Python 3.5 programming language.
View the complete course: http://ocw.mit.edu/6-0002F16
Instructor: John Guttag
Prof. Guttag provides an overview of the course and discusses how we use computational models to understand the world in which we live, in particular he discusses the knapsack problem and greedy algoriths.
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu
1. Introduction and Optimization Problems 40:57
2. Optimization Problems 48:04
3. Graph-theoretic Models 50:11
4. Stochastic Thinking 49:50
5. Random Walks 49:21
6. Monte Carlo Simulation 50:05
7. Confidence Intervals 50:29
8. Sampling and Standard Error 46:45
9. Understanding Experimental Data 47:06
10. Understanding Experimental Data (cont.) 50:33
11. Introduction to Machine Learning 51:31
12. Clustering 50:40
13. Classification 49:54
14. Classification and Statistical Sins 49:25
15. Statistical Sins and Wrap Up 44:43
source: MIT OpenCourseWare 2017年5月19日
MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016
This course provides students with an understanding of the role computation can play in solving problems. Student will learn to write small programs using the Python 3.5 programming language.
View the complete course: http://ocw.mit.edu/6-0002F16
Instructor: John Guttag
Prof. Guttag provides an overview of the course and discusses how we use computational models to understand the world in which we live, in particular he discusses the knapsack problem and greedy algoriths.
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu
1. Introduction and Optimization Problems 40:57
2. Optimization Problems 48:04
3. Graph-theoretic Models 50:11
4. Stochastic Thinking 49:50
5. Random Walks 49:21
6. Monte Carlo Simulation 50:05
7. Confidence Intervals 50:29
8. Sampling and Standard Error 46:45
9. Understanding Experimental Data 47:06
10. Understanding Experimental Data (cont.) 50:33
11. Introduction to Machine Learning 51:31
12. Clustering 50:40
13. Classification 49:54
14. Classification and Statistical Sins 49:25
15. Statistical Sins and Wrap Up 44:43
Subscribe to:
Posts (Atom)