流水號
80000
課號
BSE5182
課程識別碼
602 U3230
- 班次 01
- 3 學分
- 選修
生物環境系統工程學系選修
生物環境系統工程學研究所選修
統計碩士學位學程選修
胡明哲 教授
- 搜尋教師開設的課程
生物資源暨農學院 生物環境系統工程學系
- 搜尋教師開設的課程
- 三 7, 8, 9
農工十
2 類加選
修課總人數 20 人
本校 20 人
無領域專長
- 英文授課
- NTU COOL
- 核心能力與課程規劃關聯圖
- 備註本課程以英語授課。工程與環境統計領域選修課程之一。
本校選課狀況
載入中...
- 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.
- 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
- 課程要求Midterm exam, Homework, Presentation, Final project
- 預期每週課後學習時數
- Office Hour
星期四 14:00 - 17:00
- 指定閱讀The Elements of Statistical Learning/ Trevor Hastie, Robert Tibshirani, Jerome Friedman/ Springer
- 參考書目
- 評量方式
30%
Final project40%
Midterm exam & Homework30%
Presentation04/10
- 針對學生困難提供學生調整方式
調整方式 說明
- 課程進度
02/21第 1 週 02/21 Introduction 02/28第 2 週 02/28 *** No class (National Holiday) 03/06第 3 週 03/06 (3) Linear Methods for Regression: Regression, Ridge, Lasso 03/13第 4 週 03/13 (4) Linear Methods for Classification: Linear Discriminant Analysis, Logistic, Separating Hyperplane {*Presentation: 4.3 LDA} 03/20第 5 週 03/20 (5) Basis Expansion and Regularization {*Presentation: 5.9 Wavelet Smoothing} 03/27第 6 週 03/27 (7) Model Assessment and Selection {*Presentation: 7.11 Bootstrap Methods} 04/03第 7 週 04/03 (8) Model Inference and Averaging: Bayesian, Expectation-Maximization algorithm, Markov chain Monte Carlo, Bagging {*Presentation: 8.6 MCMC} 04/10第 8 週 04/10 Midterm exam 04/17第 9 週 04/17 (9) Additive Models, Trees, and Related Methods: Decision tree {*Presentation: 9.2 Tree-based methods} 04/24第 10 週 04/24 (12) Support Vector Machines and Flexible Discriminants {*Presentation: 12.2 Support Vector classifier} 05/01第 11 週 05/01 (14) Unsupervised Learning: Cluster analysis, Self-organizing maps, Principal component analysis {*Presentation: 14.5 Principal Components} 05/08第 12 週 05/08 (14) Unsupervised Learning: Multidimensional Scaling, Isomap {*Presentation: 14.9 Isometric feature mapping, ISOMAP} 05/15第 13 週 05/15 Final project 05/22第 14 週 05/22 (A) 15-min (ppt) presentation for Final project 05/29第 15 週 05/29 *** No class 06/04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)