[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

•  Week2 
• 課程投影片0_環境安裝
•  Week3
•  Week4 - 線上課程演練週
•  Week5 - 清明連假
•  Week6 
• Introduction  (課程影片0) (課程影片1)
• K-means and KNN (課程影片2) (課程影片3--Lab)

• Week7 - 第一次比賽 **
• Week8 - SVM  SVM_Lab（課程影片4）
• Week9 - Machine Learning Basics and Tips (課程影片5)
• Week10 - Neural Network (課程影片7)
• Week11 - 第二次比賽
• Week12 - test of statistical hypothesis (課程影片8)
• Week13 - CNN (課程影片9)
• Week14 - 加強學習(課程影片10)
• Week15 - 加強學習(續)(課程影片11)

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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

https://hackmd.io/@Dingjunzhe/SkRBjFlIL/%2Fk6bHBJhyRj6_B3NYjW6CtA

**[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.

[課程] 2019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

邱晴瑜，高靖珊,   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

    week 3    課程投影片   Lab
    week 4    課程投影片
    week 5    課程投影片   Lab
    week 10  PreContestFeedback Lab
    week 11  課程投影片   Lab
    week 12  課程投影片
    week 13  課程投影片
    week 15  課程投影片

Target

1.  Learn basic machine learning algorithms

2.  Use machine learning tools

3.  Understand data and its preprocessing

4.  Learning by doing and gaming.

Course Arrangement

Breakfast

Purpose: Application of unsupervised learning tools to game playing from the collected data.

Week 1: Introduction of the course and applications using ML

Week 2: Build Machine Learning Environment and brief intro to basic classifiers

·        Python sklearn

·        Unsupervised classifier

o   k-Means

o   Mean-shift

o   Gaussian Mixture Model (GMM)

·        Supervised classifier

o   kNN

o   SVM

·        Lab 1-1

Week 3: Build and Practice of the Game Platform

·        Installation of Game Platform

·        Game 1: Breakout clone

·        Try to write a novice code to play the game

·        Play the game 10000 times, record the processes.

·        Analysis be the HW. To report on Week 6.

Week 4: Support Vector Machine

·        SVM (Support Vector Machine)

·        SVC (Support Vector Classification)

·        SVR (Support Vector Regression)

·        Lab 1-2

Week 5: Multi-class clustering/classification

·        SVM

·        Hierarchical clustering

·        Lab 1-3

    Week 6: Student reports

·        Strategy report to get a high score from the breakout clone game.

·        To use ML tools is encouraged.

Lunch

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

Week 7: Decision Tree

·        Decision Tree

·        Random Forest

·        Lab 1-4

Week 8: Breakout Clone, pre-contest

Week 9: Bagging, Boosting, Adaboost

·        Lab 1-5

Week 10: Neural Network

·        MLP

o   Common aspects between SVM and MLP

o   Image pre-processing

o   Feature vector examples and comparison

§  2D image whole into 1D vector

§  Encoding and compression

o   Hidden layers and Activation functions

o   Back propagation algorithm

o   Before coding an MLP

§  Mathematical representation (Matrix forms)

·        Lab 1-6

Week 11: Installation of Game 2, Ping-Pong

·        Get Familiar with the online platform.

·        Try to write a novice code to play the game.

·        Play the game 10000 times, record the processes.

·        Analysis be the HW. To report on Week 14.

Week 12: DNN

·        DNN (Deep Neural Network)

o   Pitch detection: RWC dataset

o   Ping-Pong

·        Lab 1-7

Week 13: Breakout Clone contest

Afternoon Tea

Purpose: Application of machine learning tools to a computer Ping-Pong game. Object positions provided.

Week 14: Student reports

·        Strategy report to get a high score from the Ping-Pong game.

·        To use ML tools is encouraged.

Week 15: CNN

·        CNN (Convolutional Neural Network)

·        Common and different aspects between MLP and CNN

o   One big 2D convolution, Many smaller 2D convolutions, and Inner-products

o   Block-based activation (kernel Pooling) v.s. Activations

·        YOLO-like CNNs

o   Annotations

·        Lab 1-8

Week 16: RNN

·        Recurrent Neural Network

o    Stock price

·        Lab 1-9

Showtime

Purpose: Applications of ML knowledge in Game playing 

Week 17: Discussion

·        Private communication

    Week 18: Final contest and report

Grading by Ranking

1. G-1: Lab report (HackMD) and attendance: 20%
2. G-2: Breakout Clone pre-contest: 5%
3. G-3: Breakout Clone contest 2: 25%
4. G-4: Github: 10%
5. G-5: Final Contest: 45%

·        Winner of each contest with Rank=0. The second position with Rank=1. Same for others.

·        Grade = Contest highest grade (1 - Rank * factor), factor TBD

·        Finale =  sum (G-i), for all i.