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課程名稱 |
統計學習初論
Statistical Learning：Theory and Applications |
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開課學期 |
106-2 |
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授課對象 |
管理學院 工商管理學系 |
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授課教師 |
盧信銘 |
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課號 |
IM5044 |
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課程識別碼 |
725 U3550 |
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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 |
管二303 |
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備註 |
本課程中文授課,使用英文教科書。
總人數上限：50人 |
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Ceiba 課程網頁 |
http://ceiba.ntu.edu.tw/1062SL1 |
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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 |
Statistical learning refers to a set of tools for modeling and understanding complex datasets. It is a recently developed area in statistics and blends with parallel developments in computer sciences and machine learning. The field encompasses many methods such as the regularized regression, classification, graphic models, and approximation inference. This course is appropriate for master's students and advanced undergraduates who wish to use statistical learning and machine learning tools to analyze their data. |
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課程目標 |
The goal of this course is to introduce a set of tools for data analytics. We will cover the principles and applications of these models/tools. These tools will not be viewed as black boxes. Instead, students will be exposed to the details, not just the use, of these tools. The main reason is that no single approach will perform well in all possible applications. Without understanding how a tool work, it is impossible to select the best tool. |
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課程要求 |
Homework (R-based) (7-10 Homework) 64%
Self-assessment Homework (8-12 Assignments) 8%
Attendance, participation & quizzes 8%
Final Project 20%
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預期每週課後學習時數 |
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Office Hours |
另約時間 |
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指定閱讀 |
Pattern Recognition and Machine Learning by Christopher M. Bishop; ISBN 0-387-31073-8. |
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參考書目 |
待補 |
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評量方式
(僅供參考) |
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週次 |
日期 |
單元主題 |
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第1週 |
2/27 |
Introduction and review of probability theory (HW0) |
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第2週 |
3/6 |
Regressions (Part 1),
R and RSAND, HW1 |
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第3週 |
3/13 |
Regression (Part 2) (HW2)
Video (最後半小時): https://www.youtube.com/playlist?list=PLvVPTibZrkBqhVZism9gpXmF9ErWe7TYp |
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第4週 |
3/20 |
Regression (Part 3)
Class video: https://www.youtube.com/playlist?list=PLvVPTibZrkBrLuqPsQ8i8z049ZLiT4fLP
Evidence Approximation 加強說明: https://www.youtube.com/playlist?list=PLvVPTibZrkBqfya8MCijxsPl2IgkVtR0G |
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第5週 |
3/27 |
Linear Classification Models (Part 1) (HW3)
Video (後兩小時，第一節沒錄到聲音):https://www.youtube.com/playlist?list=PLvVPTibZrkBrLOZ28w9rMA1ULp5ibjHi5 |
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第6週 |
4/3 |
Holiday, No Class. |
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第7週 |
4/10 |
Linear Classification Models (Part 2)
上課影片: https://www.youtube.com/playlist?list=PLvVPTibZrkBoj0uaZ4ZZhrxzvmbaZN7VL
Youtube Video (05c): https://www.youtube.com/playlist?list=PLvVPTibZrkBpC2Ga8xizhOH1ObiYz8wzq |
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第8週 |
4/17 |
Performance Evaluation (Part 2) (HW4) |
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第9週 |
4/24 |
Performance Evaluation, Feature Selection (期末分組) |
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第10週 |
5/1 |
Tree-based Models (HW5) |
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第11週 |
5/8 |
Tree-based Model, Graphical Models (期末報告題目確定) |
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第12週 |
5/15 |
Graphical Models (HW6 Part1) |
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第13週 |
5/22 |
Graphical Models, Chinese Word Segmentation (HW6 Part 2) |
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第14週 |
5/29 |
Mixture Models and EM Algorithm |
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第15週 |
6/5 |
Topic Models (HW7) |
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第16週 |
6/12 |
No Class. Office Hour (11am - 12pm) 管理二館509室 |
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第17週 |
6/19 |
Final Project Presentation |
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