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1301 Probability 

  • Course Time: Mon 09:10-12:00

  • Classroom: AT242

  • Course Outlines: 

    • 1. Experiments, Models and Probabilities;

    • 2. Sequential Experiments;

    • 3. Discrete Random Variables;

    • 4. Continuous Random Variables.

    • 5. Multiple Random Variables;

    • 6. Probability Models of Derived Random Variables;

    • 7. Conditional Probability Models;

    • 8. Random Vectors;

    • 9. Sums of Random Variables;

    • 10. The Sample Mean;

    • 11. Hypothesis Testing;

    • 12. Estimation of a Random Variable;

    • 13. Stochastic Processes.

  • Textbook: Probability and Stochastic Processes: A Friendly Introduction for Electrical and Computer Engineers ", the 3rd Edition, by Roy D. Yates and David J. Goodman   (John Wiley & Sons), 2015.

  • Reference Books:  

    • Introduction to Statistical Pattern Recognition, by Keinosuke Fukunaga, 2nd Edition, Academic Press, 1990.

    • Introduction to Probability and Statistics:  for Engineering and the Computing Sciences,   by J. Susan Milton, Jesse C. Arnold, Liu Kwong Ip, the McGraw Hill  Companies.

    • R in ACTION: Data analysis and graphics with R, by  Kabacoff, Robert I., Manning Publications, 2015.

  • Lecture Notes: Probability00

  • Grade:  成績  (Updated at 2020/06/29)

  • News:

    • HW1: 請寫一個程式模擬骰子丟100次的結果，把結果畫出來。 (R language)

    • 4/6 Quiz 1, range: chapter 1.

    • HW2: Quiz 2.4 (R language)

    • 4/27 0930-1130 期中考，範圍：chapter1 ~ chapter3

    • HW3: Problems 2.1.10, 2.2.10

    • HW4: Example 3.34 (R language)

    • HW5: Problems3.2.10, 3.3.13, 3.5.10

    • 5/4舉行i-Learning線上測驗，範圍為期中考範圍。

    • HW6: Problems 4.5.13, 4.6.1, 4.6.9

    • 6/8 Quiz 2, range: chapter 5.1~5.7.

    • HW7: Problems 5.1.3, 5.2.3, 5.5.6, 5.7.3

    • ​所有作業成績、期末考、期末報告成績與學期成績已公布，作業成績有疑問者請於6/30下午1400-1500找助教。學期成績有疑問者也請同時段與我聯絡。

 

6638 Pattern Recognition

  • Course Time: Tue 13:10-16:00

  • Classroom: AT338

  • Course Outlines: 

    • 1.Introduction;

    • 2.Bayes Decision Theory;

    • 3.Maximum-Likelihood and Bayesian Parameter Estimation;

    • 4.Nonparametric Techniques;

    • 5.Multilayer Neural Networks;

    • 6. Deep Learning - Convolutional Neural Networks; 

    • 7. Unsupervised Learning and Clustering.

    • 8. Feature Extraction - Linear Discriminant Analysis and Principle Component Analysis;

    • 9. Deep Learning - Autoencoder; 

  • Textbook: "Pattern Classification", by Richard O. Duda, Peter E. Hart and David G. Stork, John Wiley & Sons, 2nd edition, 2001.

  • Reference Books:  

    • Introduction to Statistical Pattern Recognition, by Keinosuke Fukunaga, 2nd Edition, Academic Press, 1990.

    • Neural Networks and Learning Machines, 3rd Edition, Simon O. Haykin, McMaster University, Ontario Canada, Pearson, 2009.

    • "Artificial Intelilgence" by Leonardo Araujo dos Santos. 2018.

    • "Deep Learning", by Ian Goodfellow, Yoshua Bengio, and Aaron Courville,  MIT Press, 2016.

  • Lecture Notes:  PR00, PR01, PR02a, PR02b, PR06,  DLCNN, PR10,FeatureExtraction

  • Grade:  成績 (Updated at 2020/06/29)

  • News: 

    • HW1: Design the Maximum A Posterior  (MAP) classifier for the UCI-WINE dataset (3 classes,  13 features).  Half of the data are used in the training phase, and the rest is for evaluation.  You can download the IRIS dataset from [WINE_UCI].  The deadline is 28th April.​

    • HW2: Compute the upper bound of Bayes error (Bhattacharyya bound) or the UCI-WINE dataset. The deadline is 12th May.​

    • 作業繳交方式：內容為主要演算法與程式片斷，包含測試資料與結果，以及簡短的討論或結論等。請把書面報告電子檔，程式及相關測試資料先以Winzip或WinRAR壓縮，並以學號為其檔名，上傳到下列的FTP網站：IP：140.120.182.118，Port: 2020，Username: prstudent，Password: pr20

    • Final Project: 期末報告每組2-4人，報告論文須為近3年的SCI論文，分組名單與論文題目摘要請於5/28交，題目內容須用到本課程所涵蓋的分類器(classifiers)，含深度學習。

    • 5/5舉行i-Learning線上測驗，範圍: Chapter1,  Chapter2 and Neural Networks。

    • HW3: Image classification with convolutional neural networks, you should use at least two techniques of the following to improve the accuracy, (1) data argumentation (2) regularization -weight decay (3) dropout (4) transfer learning.  The dataset is [here].  Half of the data are used in the training phase, and the rest is for evaluation. The deadline is 8th June.​

    • HW4: Unsupervised clustering for the UCI-WINE dataset using Kohonen network.

    • 6/9 1330-1530 Final Exam

    • 6/16, 23 期末分組報告​

    • ​所有作業成績、期末考、期末報告成績與學期成績已公布，期末考有疑問者請6/30 下午1400-1500間來找我看考卷。作業成績有疑問者請於同時段找助教。學期成績有疑問者也請同時段與我聯絡。

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7748 Image Analysis and Recognition (影像分析與辨識)

  • Course Time: Wed 18:20-21:00

  • Classroom: S113 

  • Course Outlines: This course covers fundamental concepts and methods in image analysis and recognition. The course is outlined as:

    • 1. Introduction

    • 2. Digital Image Fundamentals

    • 3. Intensity Transformations and Spatial Filtering

    • 4. Morphological Image Processing

    • 5. Image Segmentation

    • 6. Deep Learning - Convolutional Neural Networks

    • 7. Image Classification and Object Detection with Convolutional Neural Networks.

    • 8. Deep Learning - Autoencoder

    • 9. Deep Learning - Generative Adversarial Network

  • Textbook: "Digital Image Processing", by R. C. Gonzalez and R. E. Woods, 4th Edition, Pearson (開發), 2017.

  • Reference Books:  "

    • Digital Image Processing Using MATLAB", 2nd Edition, by Rafael C. Gonzalez, Richard E. Woods, Steven L. Eddins, 
      McGraw-Hill, 2009.

    • "Artificial Inteligence" by Leonardo Araujo dos Santos. 2018.

    •  "Hands-On Computer Vision with TensorFlow 2", by Benjamin Planche and Eliot Andres, Packt Publishing, 2019.

  • Lecture Notes: IAR00, 關於PPT授課著作權, Chapter1, Chapter2, Chapter3,  Chapter5, Chapter6, Chapter9, Chapter10, Chapter12

  • Grade:  成績  (Updated at 2020/07/01)

  • News:​

    • HW1: Reduce High ISO noise via Image Averaging. (at least 3 photos)​

    • HW2: Image Stitching  using affine transform and nearest neighbor (or bilinear) interpolation.

    • 作業繳交方式：內容為主要演算法與程式片斷，包含測試資料與結果，以及簡短的討論或結論等。請把書面報告電子檔，程式及相關測試資料先以Winzip或WinRAR壓縮，並以學號為其檔名，上傳到指定的FTP網站，網址等資訊隨後公布。

    • Final Project: 期末專題每組1-2人，分組名單與Proposal (one page) 請於5/28交，題目內容須與影像辨識相關。

    • 5/6舉行i-Learning線上測驗，範圍：Chapter1 , Chapter2 and Chapter3-Histogram Equallization。

    • Final Project: 期末報告每組2人，報告論文須為近3年的SCI論文，分組名單與論文題目摘要請於5/28交，題目內容須用到本課程所涵蓋的影像分析或深度學習。

    • HW3: Image enhancement by using convolution with Sobel filer, Laplacian filter, and Averaging filter. The deadline of HW1~3 is  9th June.

    • 作業FTP上傳網址：IP: 140.120.182.168，PORT: 2020, Username: iarstudent, Password: iar20

    • HW4: Image classification with convolutional neural networks, you should use at least two techniques of the following to improve the accuracy, (1) data argumentation (2) regularization -weight decay (3) dropout (4) transfer learning.  The dataset is [here].  Half of the data are used in the training phase, and the rest is for evaluation. 

    • 6/10 1830-2000 Final Exam

    • 6/17, 24 期末分組報告

    • 所有作業成績、期末考、期末報告成績與學期成績已公布，成績有疑問請於7/1上課時找助教查看確認。

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1505 Business Computer Programming (商業程式設計)

  • Course Time: Tue 18:20-20:00

  • Course Outlines: 學習R語言於統計與資料分析的應用，R語言程式設計，R語言繪圖與報表，與R語言在人工智慧等的應用。內容包含有：

    • 1. RStudio 開發環境的建置與介紹; 

    • 2. 變數型態、向量運算、函數的使用; 

    • 3. 各種資料的讀取與匯入; 

    • 4. 直覺、吸睛的繪圖技巧; 

    • 5. 原始資料的整併和取樣; 

    • 6. 字串的處理與運算; 

    • 7. 迴圈、向量等群組資料的操作; 

    • 8. 報表、簡報和網頁呈現的技巧; 

    • 9. 各種統計、迴歸資料模型的應用; 

    • 10. R語言實作於深度學習CNN

  • Textbook: R 軟體 資料分析基礎與應用 第二版 2019  (R for Everyone: Advanced Analytics and Graphics)，旗標出版公司

  • Reference Book:  R in Action: Data Analysis and Graphics With R, by Kabacoff, Robert I., Manning Pubns Co., 2015

  • Lecture Notes: BCP00, R2020

  • Grade: 成績  (Updated at 2020/06/30)

  • ​News:

    • HW1: 請寫一個程式模擬骰子丟100次的結果，計算平均值並把結果畫出來。​

    • HW2: data.frame 練習，資料為10個學生的三個科目成績。並使用$來存取欄位資料，使用mean()計算某一欄位的平均值。

    • HW3: 讀取網路上的資料檔，http://web.nchu.edu.tw/~jlwu/historypages/2020spring/iris.csv，並以data.frame儲存。

    • 4/28 1830-2000 期中考，地點：綜合大樓Y304

    • 5/5舉行i-Learning線上測驗，範圍：期中考範圍加上plot繪圖。

    • HW4: 延續HW3，把此筆IRIS資料，用ggplot2來畫四個特徵值(花瓣與花萼的長與寬)的密度分布圖。

    • HW5: 延續HW4，把此筆IRIS資料，用ggplot2來畫散佈圖(任意挑選兩個特徵值為Ｘ與Ｙ軸)，不同類別使用不同顏色來標記。

    • HW6: 使用while迴圈計算1-1000的奇數和。

    • HW7: 使用R語言求解以下國一數學題目：考慮滿足下列兩個條件的二位數:(1)個位數字的2倍減去十位數字的差大於2; (2)十位數字的3倍與個位數字的和小於23，則滿足條件最大的二位數為多少？ (重要作業，分數加倍計算)

    • HW8: 使用R語言求解以下國一數學題目：有一最簡分數，其分子、分母和為70，將其化為小數並四捨五入後為0.6，則此分數為何？ (重要作業，分數加倍計算)

    • 6/16 小考，範圍：迴圈、If else判斷式、函式。

    • ​6/23 1830期末考，地點：綜合大樓R211。

    • 所有作業成績、期末考、期末報告成績與學期成績已公布，成績有疑問請於6/30上課時找助教查看確認。

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Machine Learning (資訊科技第二專長學分班)

  • Course Time: Mon 18:20-21:00

  • Classroom: AT336

  • Course Outlines: 

    • 1.Introduction to Machine Learning;

    • 2. K-Nearest Neighbors;

    • 3. Naive Bayesian Classification;

    • 4. Decision Trees and Random Forests;

    • 5. Support Vector Machines;

    • 6.  Neural Networks;

    • 7. Unsupervised Learning and Clustering;

    • 8. Deep Learning

  • Textbook: “初探機器學習 使用Python”, O’Reilly, 2017(碁峰) (Thoughtful Machine Learning with Python: A Test-Driven Approach 1st Edition -Matthew Kirk)

  • Reference Books:  

    • “Introduction to Statistical Pattern Recognition” by Keinosuke Fukunaga, Academic Press, 2nd edition,1990.

    • “Neural Networks and Learning Machines” by Simon O. Haykin, Pearson Education, Inc., 3rd Edition, 2009.

    • “Artificial Intelligence” - Leonardo Araujo dos Santos. 2018.

  • Lecture Notes:  ML00, ML01, ML02, ML06, MLP, CNN

  • Grade:  成績 (Updated at 2020/06/29)

  • News: 

    • HW1:  Design the k nearest neighbor classifier ( kNN) for the IRIS dataset. Half of the data are used as the training set, and the other half is used for testing. Compute the classification accuracy. Note: You can download the IRIS dataset from [IRIS_UCI]. The deadline is 27th April.

    • HW2: Design the Maximum A Posterior  (MAP) classifier for the IRIS dataset. Half of the data are used as the training set, and the other half is used for testing. Compute the classification accuracy with HW1. 

    • 5/4 期中考筆試。

    • HW3: Design the Multilayer Perceptron  (MLP) classifier for the IRIS dataset using Tensorflow deep learning framework.

    • 6/22 期末考筆試。

    • 所有作業成績、期末考、與學期成績已公布，作業與期末考有疑問者請於6/29晚上與助教聯絡。

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