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課程名稱 |
機器學習與環境資料分析
Machine Learning and Environmental Data Analysis |
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開課學期 |
112-2 |
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授課對象 |
生物環境系統工程學研究所 |
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授課教師 |
胡明哲 |
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課號 |
BSE5182 |
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課程識別碼 |
602EU3230 |
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班次 |
01 |
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學分 |
3.0 |
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全/半年 |
半年 |
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必/選修 |
選修 |
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上課時間 |
星期三7,8,9(14:20~17:20) |
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上課地點 |
農工十 |
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備註 |
本課程以英語授課。
總人數上限：20人 |
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課程簡介影片 |
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核心能力關聯 |
核心能力與課程規劃關聯圖 |
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課程大綱
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課程概述 |
The science of machine learning plays a key role in the fields of statistics, data mining and artificial intelligence, intersecting with areas of engineering and other disciplines. This course describes some of the most important techniques of machine learning and environmental data analysis. |
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課程目標 |
(1) Introduction
(2) Overview of Supervised Learning
(3) Linear Methods for Regression
(4) Linear Methods for Classification
(5) Basis Expansions and Regularization
(6) Kernel Smoothing Methods
(7) Model Assessment and Selection
(8) Model Inference and Averaging
(9) Additive Models, Trees, and Related Methods
(10) Boosting and Additive Trees
(11) Neural Networks
(12) Support Vector Machines and Flexible Discriminants
(13) Prototype Methods and Nearest-Neighbors
(14) Unsupervised Learning
(15) Random Forests
(16) Ensemble Learning |
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課程要求 |
Midterm exam, Homework, Presentation, Final project |
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預期每週課後學習時數 |
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Office Hours |
每週四 14:00~17:00 |
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指定閱讀 |
The Elements of Statistical Learning/ Trevor Hastie, Robert Tibshirani, Jerome Friedman/ Springer |
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參考書目 |
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評量方式
(僅供參考) |
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No. |
項目 |
百分比 |
說明 |
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1. |
Final project |
30% |
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2. |
Midterm exam & Homework |
40% |
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3. |
Presentation |
30% |
04/10 |
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週次 |
日期 |
單元主題 |
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第1週 |
02/21 |
Introduction |
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第2週 |
02/28 |
*** No class (National Holiday) |
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第3週 |
03/06 |
(3) Linear Methods for Regression: Regression, Ridge, Lasso |
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第4週 |
03/13 |
(4) Linear Methods for Classification: Linear Discriminant Analysis, Logistic, Separating Hyperplane {*Presentation: 4.3 LDA} |
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第5週 |
03/20 |
(5) Basis Expansion and Regularization {*Presentation: 5.9 Wavelet Smoothing} |
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第6週 |
03/27 |
(7) Model Assessment and Selection {*Presentation: 7.11 Bootstrap Methods} |
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第7週 |
04/03 |
(8) Model Inference and Averaging: Bayesian, Expectation-Maximization algorithm, Markov chain Monte Carlo, Bagging {*Presentation: 8.6 MCMC} |
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第8週 |
04/10 |
Midterm exam |
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第9週 |
04/17 |
(9) Additive Models, Trees, and Related Methods: Decision tree {*Presentation: 9.2 Tree-based methods} |
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第10週 |
04/24 |
(12) Support Vector Machines and Flexible Discriminants {*Presentation: 12.2 Support Vector classifier} |
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第11週 |
05/01 |
(14) Unsupervised Learning: Cluster analysis, Self-organizing maps, Principal component analysis {*Presentation: 14.5 Principal Components} |
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第12週 |
05/08 |
(14) Unsupervised Learning: Multidimensional Scaling, Isomap {*Presentation: 14.9 Isometric feature mapping, ISOMAP} |
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第13週 |
05/15 |
Final project |
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第14週 |
05/22 |
(A) 15-min (ppt) presentation for Final project |
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第15週 |
05/29 |
*** No class |
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第16週 |
06/04 |
(B) Poster session and 5-min (poster) presentation for final project:
* Time: Tuesday, June 4th, 12:20-14:20
* Location: Shih Sun-Fu meeting room (施孫富會議室)
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