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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 |
108-2 |
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授課對象 |
生物資源暨農學院 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 |
BSE5162 |
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課程識別碼 |
622 U5170 |
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班次 |
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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 |
星期二2,3,4(9:10~12:10) |
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上課地點 |
農工十 |
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備註 |
總人數上限：30人 |
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Ceiba 課程網頁 |
http://ceiba.ntu.edu.tw/1082BSE5162_ |
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課程簡介影片 |
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核心能力關聯 |
本課程尚未建立核心能力關連 |
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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
(Free download from NTU library) |
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參考書目 |
The Elements of Statistical Learning/ Trevor Hastie, Robert Tibshirani, Jerome Friedman/ Springer |
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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. |
Midterm exam |
30% |
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2. |
Homework & presentation |
40% |
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3. |
Final project |
30% |
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週次 |
日期 |
單元主題 |
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第1週 |
3/03 |
(3) Linear Methods for Regression: Regression, Ridge, Lasso |
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第2週 |
3/10 |
(4) Linear Methods for Classification: Linear Discriminant Analysis, Logistic, Separating Hyperplane {*Presentation: 4.3 LDA} |
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第3週 |
3/17 |
(5) Basis Expansion and Regularization {*Presentation: 5.9 Wavelet Smoothing} |
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第4週 |
3/24 |
(7) Model Assessment and Selection {*Presentation: 7.11 Bootstrap Methods} |
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第5週 |
3/31 |
(8) Model Inference and Averaging: Bayesian, Expectation-Maximization algorithm, Markov chain Monte Carlo, Bagging {*Presentation: 8.6 MCMC} |
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第6週 |
4/07 |
(9) Additive Models, Trees, and Related Methods: Decision tree {*Presentation: 9.2 Tree-based methods} |
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第7週 |
4/14 |
(11) Neural Networks {*Presentation: 11.3 & 11.4 Neural networks and fitting} |
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第8週 |
4/21 |
Midterm |
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第9週 |
4/28 |
(12) Support Vector Machines and Flexible Discriminants {*Presentation: 12.2 Support Vector classifier} |
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第10週 |
5/05 |
(13) Prototype Methods and Nearest-Neighbors {*Presentation: 13.2 Nearest-Neighbors} |
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第11週 |
5/12 |
(14) Unsupervised Learning: Cluster analysis, Self-organizing maps, Principal component analysis {*Presentation: 14.5} |
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第12週 |
5/19 |
(14) Unsupervised Learning: Multidimensional Scaling, Isomap {*Presentation: 14.10} |
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第13週 |
5/26 |
(15) Random Forests {*Presentation: 15.2} |
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第14週 |
6/02 |
(16) Ensemble Learning {*Presentation: 16.3} |
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第15週 |
6/09 |
Final project |
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第16週 |
6/16 |
繳交期末報告 |
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