2020年3月13日 星期五

[課程] 2020基於遊戲的機器學習入門

Instructor
蘇文鈺 (Alvin W.Y. Su, alvinsu@mail.ncku.edu.tw)

Teaching Assistant
陳麒麟,丁俊哲,戴逸任,陳如琰 RM 65707

Course Materials
  1. Introduction to machine learning textbook
  2. Gamed Based coding platform
  3. Online ppt
  4. Reference: Stanford CS229, https://stanford.edu/~shervine/teaching/cs-229.html


Power Point

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**[第一次比賽_注意事項]
1.登入PassCode 請輸入 CSIEML (請參考沒有聲音的影片)
2.請留下一個版本就好,其餘的版本請刪除,以利考試運作。(沒有刪除斟酌扣0.5分)
3. 4/15(三) 12:00 ~ 4/16(四) 09:00 登入上傳
  4/16(四) 12:00 前公布考試順序

  4/16(四) 16:00 直播考試結果

4. Office Hour 4/15(三) 19:00-21:00 65705
5. 示範影片

**[第二次比賽公告]
公告如下

**[第三次比賽公告]

考試公告如下
https://hackmd.io/@Dingjunzhe/SkRBjFlIL/%2Fi6GeXYnuTOeuiSfKqw6y3w

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    Course Arrangement

    1st quarter

    Purpose: Get familiar with the course requirement, the offline game platform and the online game platform. Collect and annotate the data.

    week 1 : Introduction of the course and applications using ML. How this course will proceed this semester. Especially in the COVID-19 period.

    week 2 : Setup of Python programming environment. Use of Github.

    week 3 : Build and Practice of the offline Game Platform and the online Game Platform.

    week 4 : Practice using the online Game Platform. Intro. to basic classifiers

    2nd quarter

    Purpose: Application of simple machine learning tools to a computer game, called Breakout Clone, also called Arkanoid. Object positions provided.

    week 5 : 兒童節、民族掃墓節補假。

    Week 6: Introduction and models

    Week 7: 1st online contest, New Breakout Clone (Behavior is a little bit different!) (10%)

    Week 8: Support Vector Machine/Multi-class clustering/classification

    3rd quarter

    Purpose: Application of more machine learning tools to New Pong and Racing Cars.

    Week 9: 2nd online contest. New Pong (15%)

    Week 10: Bagging, Boosting, Adaboost

    Week 11: Neural Network

    Week 12: 3rd contest. Racing Cars 1. (20%)

    Final quarter

    Purpose: Application of machine learning tools to Racing Cars 2. Object positions provided.

    Week 13: DNN

    Week 14: CNN

    Week 15: RNN

    Week 16: Team Report

    Week 17: 端午節(放假)。

    Week 18: Final contest (40%) and final report (5%)




    Target
    1.  Learn basic machine learning algorithms
    2.  Use machine learning tools
    3.  Understand data and its preprocessing
    4.  Learning by doing and gaming.


    Grading by Rank


    Reports : 2+3+5+5= 15%   
    Contest 1: 10%   
    Contest 2: 15%   
    Contest 3: 20%   
    Contest 4: 40%   
    Bonus: 10%


    Cours Preparation Items
                     All reports in HackMD 
                     All works in Github 
                     All contest results on the online platform 
                     You may need to learn many materials from online courses and/or videos.