1341 Probability (機率)
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Course Time: Mon 09:10-12:00
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Classroom: AT338
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Course Outlines:
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1. Experiments, Models and Probabilities;
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2. Sequential Experiments;
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3. Discrete Random Variables;
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4. Continuous Random Variables.
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5. Multiple Random Variables;
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6. Probability Models of Derived Random Variables;
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7. Conditional Probability Models;
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8. Random Vectors;
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9. Sums of Random Variables;
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10. The Sample Mean;
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11. Hypothesis Testing;
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12. Estimation of a Random Variable;
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13. Stochastic Processes.
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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.
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Reference Books:
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Introduction to Statistical Pattern Recognition, by Keinosuke Fukunaga, 2nd Edition, Academic Press, 1990.
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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.
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R in ACTION: Data analysis and graphics with R, by Kabacoff, Robert I., Manning Publications, 2015.
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Grade: 成績 (Updated at 2021/1/13)
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News:
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9/14的課，因到農委會科技中心出差，暫停一次，補課時間另訂。
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HW1: Problems 1.1.2, 1.3.1, and 1.5.7
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HW2: R language - Quiz 2.4
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HW3: Problems 2.1.10 and 2.2.10
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HW4: R language - Example 3.34
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HW5: Problems 3.2.10, 3.3.12 and 3.5.9
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11/2 0930-1130 期中考，範圍：chapter1 ~ chapter3
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HW6: Problems 4.6.15 and 4.7.2, R language: Simulate N=50 data with Gaussian distribution N(60,10), calculate mean, std and median.
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12/7: Quiz, range: chapter 4.
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12/21小考，範圍5-1 ~5-8。
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期末考第6題題目有誤，送分。期末考與學期成績已公布，期末考成績有疑問者請於1/13下午1400-1600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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6653&7766 Digital Image Processing (影像處理)
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Course Time: Tue 13:10-16:00 / Mon 18:20-21:00
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Classroom: AT336 / AT338
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Course Outlines: This course covers fundamental concepts and methods in digital image processing and their applications. The course is outlined as:
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1 Introduction
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2 Digital Image Fundamentals
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3 Intensity Transformations and Spatial Filtering
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4 Filtering in the Frequency Domain
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5 Image Restoration and Reconstruction
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6 Wavelet and Other Image Transforms
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7 Color Image Processing
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8 Morphological Image Processing
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9 Image Segmentation
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10 Deep Learning
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11 Image Classification and Object Detection with Convolutional Neural Networks.
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Textbook: "Digital Image Processing", by R. C. Gonzalez and R. E. Woods, 4th Edition, Pearson (開發), 2017.
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Reference Books: "
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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.
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"Hands-On Computer Vision with TensorFlow 2", by Benjamin Planche and Eliot Andres, Packt Publishing, 2019.
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Lecture Notes:Chapter0, 關於PPT授課著作權, Chapter1, Chapter2, Chapter3, Chapter5, Chapter6, Chapter9, Chapter10, Chapter12
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Grade: 碩博成績 (Updated at 2021/1/13) ，碩專成績 (Updated at 2021/1/13)
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News:
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9/14 晚上在職班影像處理暫停一次，補課時間另訂。
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9/29 課程暫停一次，補課時間另訂。
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10/13 下午舉辦「影像辨識工作坊」。建立上課 Python 開發環境影片如下：
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11/10 課程因到農委會開會暫停一次，11/13周五下午1310補課，地點：應科336教室。
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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。
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期末報告，每組報告一篇近五年SCIE期刊論文，報告時間為15分鐘(含QA)。(碩博班3-4人一組，碩專班1-2人一組)。碩博期末報告：12/22, 29；碩專期末報告：12/21, 28。
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期末筆試，碩博：1/5，碩專：1/4
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作業繳交資訊：主機:140.120.182.145，連接埠:1023。碩博班：使用者名稱:ipstudent，密碼:ip20。碩專班：使用者名稱:ipstudentn，密碼:ip20。
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HW3: Morphology: Boundary extraction and Region Filling.
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HW4: Use a CNN-based neural network to classify the scene is indoors or outdoors. 影像上傳網址如下：FTP IP: 140.120.182.22，user: ipstudent，password: ip2020，port: 1023
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期末考，期末報告與學期成績已公布，期末考與作業成績有疑問者請於1/13下午1400-1600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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4505 Information Management (資訊管理)
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Course Time: Tue 18:20-20:05
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Classroom: L216 (水保系2F 計中第一PC教室)
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Course Outlines: 本課程資訊系統的理論與實務，讓同學瞭解管理資訊系統理論在實務面之應用。課程大綱如下：
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1. 資訊系統簡介
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2. 企業流程再造
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3. 資訊系統分析與設計
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4. 專案管理
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5. 企業資源規劃
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6. 供應鏈管理
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7. 客戶關係管理
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8. 電子資料交換系統
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9. 決策支援
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10. 資料探勷
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11. 電子商務
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12. 雲端運算
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13. 資料庫管理
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14. 網路管理
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15. 資訊安全
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16. 大數據－Big Data
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17. 物聯網
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Textbook: 資訊管理概論(第三版), 陳瑞順, 全華圖書出版, 出版日期：2015/09/1
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Grade: 成績 (Updated at 2021/1/11)
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News:
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HW1: 何謂UML，請舉圖例說明。
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9/29 課程暫停一次，補課時間另訂。
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HW2 : 資訊系統應用的個案分析。
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10/27: 小考關聯式資料庫。
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11/3 期中考
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12/1 小考
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期末報告，每組1-2人。題目：資訊系統於企業的實際應用與個案分析探討，每組口頭報告時間為15分鐘(含QA)。每組須繳交一份至少5頁的期末書面報告，內容須包含：產業概況、個案背景、資訊系統導入過程、資訊系統導入後的優勢或相關問題討論，結論與心得。報告日期：12/29。
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1/5 期末考
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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1/13下午1400-1600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)
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Course Time: August 24th, 25th, and 27th 09:10-16:00
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Classroom: S821 (Room 821@Science Building)
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Course Outlines:
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1. Introduction to Machine Learning
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2. Multilayer Perceptron
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3.Deep Convolutional Neural Networks
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4.Object Detection and Classification
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5.Deep learning on the Raspberry Pi.
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- Reference Books:
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[1] "Artificial Inteligence" by Leonardo Araujo dos Santos. 2018.
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[2] "Hands-On Computer Vision with TensorFlow 2", by Benjamin Planche and Eliot Andres, Packt Publishing, 2019.
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[3] “Neural Networks and Learning Machines” by Simon O. Haykin, 3rd Edition, 2009
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Grade: 成績 (Updated at 2020/12/27)
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News:
2273 Data Analysis and Graphics and Machine Learning with R (R語言實務與機器學習)
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Course Time: August 31th, September 1st and 3rd, 09:10-16:00
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Classroom: S821 (Room 821@Science Building)
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Course Outlines:
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1. RStudio 開發環境的建置與介紹
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2. 變數型態、向量運算、函數的使用
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3. 各種資料的讀取與匯入
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4. 直覺、吸睛的繪圖技巧
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5. 原始資料的整併和取樣
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6. 字串的處理與運算
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7. 迴圈、向量等群組資料的操作
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8. 報表、簡報和網頁呈現的技巧
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9. 各種統計、迴歸資料模型的應用
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10. R 軟體於機器學習與深度學習(CNN)的應用
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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
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Reference Books: R IN ACTION: Data analysis and graphics with R, by Kabacoff, Robert I., Manning Publications, 2015.
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Lecture Notes: R_Summer
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Grade: 成績 (Updated at 2020/12/27)
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News:
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Teaching Materials: (1) CSV sample file: Tomato, Hospital, (2) Sample code1 (2) 語言自訂函數範例
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R 安裝機器學習套件 RTENSORFLOW, KERAS，請見：R_Keras
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