Jiunn-Lin Wu, PhD

1341 Probability (機率)

• Course Time: Mon 09:10-12:00

• Classroom: AT338

• 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: Probability0 

• Grade:  成績  (Updated at 2022/1/20) 

• News:

  • 10/3前的課使用Google Meet線上上課，會點名，網址為：meet.google.com/snq-rhtq-san

  • 期末考，學期成績已公布，期末考成績有疑問者請於1/20前與助教聯絡，學期成績有疑問者請於1/21早上1000-1130與我聯絡，逾時不後。

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​​6653&7766 Digital Image Processing (影像處理)

• Course Time: Tue 13:10-16:00 / Mon 18:20-21:00

• Classroom: AT336 / AT338 

• Course Outlines: This course covers fundamental concepts and methods in digital image processing and their applications. The course is outlined as:

  • 1 Introduction

  • 2 Digital Image Fundamentals

  • 3 Intensity Transformations and Spatial Filtering

  • 4 Filtering in the Frequency Domain

  • 5 Image Restoration and Reconstruction

  • 6 Wavelet and Other Image Transforms

  • 7 Color Image Processing

  • 8 Morphological Image Processing

  • 9 Image Segmentation

  • 10 Deep Learning

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

• 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 Intelligence" by Leonardo Araujo dos Santos. 2018.

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

• Lecture Notes: Chapter0, 關於PPT授課著作權, Chapter1, Chapter2, Chapter3,  Chapter5, Chapter6, Chapter9, Chapter10, Chapter12

• Grade: 碩博成績  ， 碩專成績   (Updated at 2022/1/20) 

• News:

  • 10/3前，碩博班的課使用Google Meet線上上課，會點名，網址為： meet.google.com/rao-jsmb-duw  ​​

  • 10/3前，碩專班的課使用Google Meet線上上課，會點名，網址為： meet.google.com/dvf-eiyi-eye

  • 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WORD電子檔，程式Source code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FTP網站，網址資訊隨後公布。

  • 作業繳交資訊：主機:140.120.182.172，連接埠:1023。碩博班：使用者名稱:ipstudent，密碼:ip2021。碩專班：使用者名稱:ipnstudent，密碼:ip2021。

  • 期末考，期末報告與學期成績已公布，期末考成績有疑問者請於1/21早上1000-1130找助教，學期成績有疑問者也請同時段與我聯絡，逾時不後。

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1923 Introduction to Deep Learning in Image Processing (深度學習概論與影像處理應用)

• Course Time: August 24th, 25th, and 27th  09:10-16:00

• Classroom: S821 (Room 821@Science Building)

• Course Outlines:

  • 1. Introduction to Machine Learning

  • 2. Multilayer Perceptron

  • 3.Deep Convolutional Neural Networks

  • 4.Object Detection and Classification

  • 5.Deep learning on the Raspberry Pi.

• Lecture Notes:  ML, MLP, DL

• Reference Books: 

  • [1] "Artificial Inteligence" by Leonardo Araujo dos Santos. 2018.

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

  • [3] “Neural Networks and Learning Machines” by Simon O. Haykin, 3rd Edition, 2009​

• Grade: 成績  (Updated at 2022/1/13) 

• News: ​

  • IRIS dataset  (IRIS資料集簡介與來源-UCI Machine Learning Repository)

  • Machine learning and Deep learning Sample codes (Tensorflow 2.0)

 

1922  Data Analysis and Graphics and Machine Learning with R (R語言實務與機器學習)

• Course Time: August 31th, September 1st and 3rd,  09:10-16:00

• Classroom: S821 (Room 821@Science Building)

• Course Outlines: 

  • 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 軟體於機器學習與深度學習(CNN)的應用

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

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

• Lecture Notes: R_Summer

• Grade: 成績  (Updated at 2022/1/14) 

• News:

  • Teaching Materials: (1) CSV sample file: Tomato, Hospital,  (2) Sample code1 (2) 語言自訂函數範例

  • R 安裝機器學習套件 RTENSORFLOW, KERAS，請見：R_Keras

  • IRIS dataset  (IRIS資料集簡介與來源-UCI Machine Learning Repository)

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Artificial Intelligence (人工智慧，資訊科技第二專長學分班)

• Course Time:  Tue. 18:30-21:00

• 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:  AI0

• Grade:  成績  (Updated at 2022/1/20) 

• News: 

  • 10/3前的課使用Google Meet線上上課，會點名，網址為：meet.google.com/ofs-uuwa-uqe

  • Machine learning Sample codes (Tensorflow 2.0)

  • 期末考與學期成績成績已公布，疑問者請於1/21早上1000-1130與我聯絡，逾時不後。

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