課程資訊
課程名稱
統計學習與深度學習
Statistical Learning and Deep Learning 
開課學期
109-1 
授課對象
管理學院  資訊管理學系  
授課教師
盧信銘 
課號
IM5056 
課程識別碼
725 U3670 
班次
 
學分
3.0 
全/半年
半年 
必/選修
選修 
上課時間
星期四2,3,4(9:10~12:10) 
上課地點
管二305 
備註
商業資料分析學分學程課程
總人數上限:70人 
Ceiba 課程網頁
http://ceiba.ntu.edu.tw/1091sldl 
課程簡介影片
 
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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 recently deep learning. 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. This course is appropriate for master's students and advanced undergraduates who wish to use statistical learning, machine learning, and deep learning to analyze their data. 

課程目標
Omitted. 
課程要求
*Grade Distribution*
The course grades will be determined by the following percentages:

Homework (Python-based) (5-6 Assignments) 55%
Attendance, participation & quizzes 15%
Final Project (Presentation) 30%
Total 100%


*Computational Tools*
Students are required to use Python 3 (with scikit-learn, pandas, matplotlib, numpy, pytorch, etc.) to solve homework assignments.


*Homework*
There are at least five graded assignments. Unless otherwise stated, students are required to organize their code and results using Jupyter Lab and submit their homework to NTUCOOL using the IPYNB format. An assignment is due at the beginning of the first class in the following week. Late submissions will not be accepted. Homework assignments play a very important role in the learning process, and students are expected to spend a significant amount of time in solving homework problems. Students are allowed to discuss about homework questions. However, each student must turn in her/his own homework. Plagiarism will result in severe penalty for everyone involved.

*Final Project (Team-based)*
Students are expected to form teams of three to six people and work on a data analytics problem that is interesting and challenging for you. Details will be given in class.
 
預期每週課後學習時數
 
Office Hours
 
參考書目
待補 
指定閱讀
* Pattern Recognition and Machine Learning by Christopher M. Bishop; ISBN 0-387-31073-8.
* Hands-on Machine Learning with Scikit-Learn & Tensorflow by Aurelien Geron; ISBN 978-1-491-96229-9.
* Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville; https://www.deeplearningbook.org/
* Dive into Deep Learning by Aston Zhang, Zack C. Lipton, Mu Li, Alex J. Smola; https://d2l.ai/ and https://github.com/dsgiitr/d2l-pytorch
 
評量方式
(僅供參考)
   
課程進度
週次
日期
單元主題
第1週
9/17  Introduction, Regression Part 1 (K-nearest-neighbors) 
第2週
9/24  Regression Parts 2-3 (Linear models and regularization)  
第3週
10/01  Holiday, no class 
第4週
10/08  Regression Part2 4-6: Dummy coding, bias-variance trade off, and an example  
第5週
10/15  Linear Models for Classification 
第6週
10/22  Model Evaluation 
第7週
10/29  Feature Selection 
第8週
11/05  Dimension Reduction 
第9週
11/12  Tree-based Models, Part 1 
第10週
11/19  Tree-based Models, Part 2: Bagging, Random Forest, Boosting, and Stacking 
第11週
11/26  Deep Feedforward Networks 
第12週
12/03  Distributed Representations for Natural Languages 
第13週
12/10  Regularizations and Optimizations 
第14週
12/17  Convolutional Network, Part 1 
第15週
12/24  Convolutional Network, Part 2 
第16週
12/31  Final Project Presentation 
第17週
1/07  TBD