課程資訊



一、課程內容

1.   講義上課(隨機點名十次 計分20%)

2.   繳交習題(八至十題 附有範例，審核內容，計分60%)

3.   期末專題(較大型作業 計分20%)

4.   沒有考試

二、參考書籍

1.       Computational Thinking and Problem Solving

Guttag, John. “Introduction to Computation and Programming Using Python: With Application to Understanding Data.” 2nd ed. MIT Press, 2016.

2.       自編講義(Moodle 可事先下載)

三、教材內容

1.      Python 程式語言簡介   

                                              i.             Python Data Structure

                                             ii.             Python Classes

2.      探索最佳化問題(Introduction to Optimization Problems)

                                               i.             Knapsack Problems

                                             ii.             Greedy algorithm for solving problems

3.      最佳化問題初解(Optimization Problems Solutions)      

                                               i.             NP-complete Problems

                                             ii.             Dynamic Programming

4.      圖形理論模式(Graph- Models)    

                                               i.             Classic Graph-Theoretic Problems

                                             ii.             Graph Searching Algorithm

5.      隨機思考(Stochastic Thinking)

                                               i.             Uncertainty World and Stochastic Modeling

                                             ii.             Stochastic Programs

6.      隨機漫步(Random Walks)     

                                               i.             Random Walks

                                             ii.             The Drunkard’s Walk

                                          iii.             Biased Random Walks

7.      蒙地卡羅模擬(Monte Carlo Simulation)

                                               i.             Pascal’s Problem

                                             ii.             Probability Problems and solutions

8.      信賴區間(Confidence Intervals)   

                                               i.             Data analysis

                                             ii.             Confidence Intervals

                                          iii.             Central Limit Theorem

9.      取樣及標準差(Sampling and Standard Error)  

                                               i.             Data Sampling

                                             ii.             Standard Error of the Mean

10. 認識實驗資料(Understanding Experimental Data)

                                               i.             Behavior of Experiments

                                             ii.             Know Experiment Data

11. 認識實驗資料二(Understanding Experimental Data (cont.)

12. Fitting Exponentially Distributed Data (Python)

 

13. 機器學習簡介(Introduction to Machine Learning)

                                               i.             Feature Vectors

                                             ii.             Distance Metrics

14. 資料分群簡介(Introduction to Clustering)

                                               i.             Class Cluster

                                             ii.             K-means Clustering

                                          iii.             Other Clustering Methods

15. 資料分類簡介 (Introduction to Classification)

                                               i.             Knowing Classifiers

                                             ii.             Classification Example

                                          iii.             K-nearest Neighbors

                                          iv.              Regression-based Classifiers

16. 資料分類及統計誤差(Classification and Statistical Sins)      

                                               i.             Tests Are Imperfect

                                             ii.             Sampling Bias

                                          iii.             Statistically Significant Differences Can Be Insignificant

17. 期末專題製作