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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: Probability08

  • Grade: 成績 (Updated at June 28th, 2018)

  • News:

    • HW1: Quiz 1.6 using R language, and problems 1.1.2, 1.3.1, 1.4.2.

    • HW2: Monty Hall Game, but Monty knows the answer. The deadline for HW1&2 is 26th March.

    • HW3: Quiz 2.4 using R language, and problems 2.1.7, 2.2.1. The deadline for HW3 is 9th April.

    • Midterm Exam at 0930, 23th April.  The range is from Chapter 1 to Chapter 3.

    • HW4: Example 3.34 using R language. The deadline is 7th May.

    • HW5: Problems 4.5.13 and 4.6.9，R language: Simulate  the score of 50 students with Gaussian distribution N(60,10), then compute the mean, std, and median. 

    • Quiz-Chapter 5 at June 11th.

    • Quiz與HW等成績已公布，有疑問者請於6/25與助教聯絡，逾時不後。

    • 期末考成績已公佈，有疑問者請於本周四(6/28) 1400-1700與助教(應科R347)聯絡，逾時不後。

    • 學期成績已公佈，有疑問者請於本周五(6/29) 0800-1100與我聯絡，逾時不後。

 

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.Linear Discriminant Functions; 6.Multilayer Neural Networks; 7. Nonperametric Methods; 8. Algorithm Independent Machine Learning ; 9.Unsupervised Learning and Clustering.

  • 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.

    • Statistical Pattern Recognition, by Andrew Webb, Second Edition, John Wiley & Sons, 2002.

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

  • Lecture Notes: PR00, PR01, MathFoundations, RandomVectors, PR02a, PR02b, ParametricClassifiers, PR06, PR10, FeatureExtraction 

  • Grade: 成績 (Updated at July 2nd, 2018)

  • News: 

    • 請同學踴躍參加「跨領域工程教育人才培育與研究」活動-專題演講 "創意設計"，日期時間為5/11日下午14:00-17:00。

    • HW1:  Design the k nearest neighbor classifier (kNN) for the WINE dataset if the half of the data are used as the training set, and the other half is used for testing. Compare the classification accuracy of training data and testing data, and the computation time. Note: You can download the WINE dataset from [wine.data], its detail information is here. The WINE dataset is the results of a chemical analysis of wines grown in the same region in Italy but derived from three different cultivars. The analysis determined the quantities of 13 constituents found in each of the three types of wines.  It is from http://archive.ics.uci.edu/ml/datasets/Wine. The deadline is 9th April.

    • 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，上傳到以下指定的FTP網站：IP:140.120.182.74, Port:1023, Username:prstudent, Password: pr18

    • HW2: Generate the Gaussian distributed datasets as shown in stationary random process.pdf . Note: gasdev.pdf is a C++ function to return a normally distributed deviate with zero mean and unit variance from a uniform deviate. The deadline is 1st May.

    • HW3: Design the Maximum A Posterior  (MAP) classifier for the WINE dataset if half of the data are used in the training phase. Compare the result with HW1 and give a brief discussion. Please also show the classification result of the dataset in HW2 using the Maximum A Posterior  (MAP) classifier if half of the data are used in the training phase.  The deadline is 15th May.

    • Final Project: 期末專題每組2-4人，分組名單與Proposal (one page) 請於5/22交，題目內容須用到本課程所涵蓋的分類器(classifiers)。表現優良的組別，推薦參加6/22下午舉行的「興創跨領域期末成果展」。

    • HW4: Design a Back-Propagation Network to solving the classification problem of WINE dataset. Half of the data are used as the training set, and the other half is used for testing. Compare the classification accuracy with the kNN classifier (HW1) and Bayes classifier (HW3). The deadline is 12th June.

    • Final Project Oral Presentation: 12th and 19th June. 每一組須交一份4-6頁的書面報告。Final Exam: 26th June.

    • 6/22興創跨領域活動地點：  綜合教學大樓中庭及109翻轉教室，時間：下午2:00~4:00，每組十分鐘。

    • 參加6/22興創跨領域活動的同學請當天下午1400到綜合教學大樓109教室準備，活動時間表請見:活動時間表。

    • 期末考與學期成績已公佈，有疑問者請於本周五(6/29) 0800-1100與我聯絡，逾時不後。

 

​3535 Digital Photography and Image Processing (數位攝影與影像處理)

  • Course Time: Tue 18:20-21:00

  • Classroom: L216 (水保系二館二樓 電腦教室)

  • Course Outlines: 1.認識數位單眼相機DSLR; 2 基礎攝影理論介紹, 3.數位暗房RAW檔的處理，以Lightroom為例; 4.Photoshop 基礎影像處理; 5.Photoshop 進階影像處理，含筆刷工具，液化工具等; 6. Photoshop 高階影像處理，含物件去背，圖層使用，影像合成，批次處理等; 7. 高動態範圍(HDR)影像製作與處理; 8. 寬景照(Panorama) 影像製作與處理; 9. 閃光燈基本理論與進階使用技巧; 10. 數位影像多媒體光碟製作; 11. 影片剪輯與後製處理，以會聲會影(Video Studio)為例; 12.  縮時攝影。

  • Textbook: 抓住你的Photoshop CS6，施威銘研究室著，旗標出版股份有限公司 (ISBN：9789863120735 )

  • Lecture Notes: 攝影理論簡介，閃光燈

  • Grade: 成績 (Updated at July 2nd, 2018)

  • News:

    • HW1: 長曝光拍照練習，拍兩張照片，一張是大光圈與高ISO，另一張是小光圈與低ISO，且兩張的曝光量一樣。

    • HW2: 權衡式測光，重點測光的實拍比較。The deadline for HW1&2 is 17th April.

    • 期中考：1830-2000, 24th April, Location: 綜合大樓Y405. 

    • HW3: 黑卡。請詳細說明亮部與暗部的測光數據與拍攝心得。 

    • HW4: 閃光燈夜景人像練習。The deadline for HW3&4 is 8th May.

    • Adobe Lightroom：官網下載，或i興雲課程目錄。

    • HW5: 五個Lightroom全域影像處理。

    • HW6: 五個Lightroom局部影像處理。The deadline for HW5&6 is 29th May.

    • Adobe Photoshop CS6 ：官網下載，或i興雲課程目錄。

    • HW7: 以PS完成彩色人像黑白背景的影像

    • HW8: 身分證照製作

    • 期末考：1830-2000, June 26th, Location: 綜合大樓Y405. 

    • 點名與作業成績已公布，有疑問者請於6/26與助教聯絡，逾時不後。

    • 期末考與學期成績已公佈，有疑問者請於本周五(6/29) 0800-1100與我聯絡，逾時不後。

 

Training Course of R Language for USI

  • Course Time: Thu 16:00-19:00

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

  • Lecture Notes: R2018

  • News: 

 

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