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課程名稱 |
統計學習與深度學習
Statistical Learning and Deep Learning |
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開課學期 |
112-1 |
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授課對象 |
管理學院 資訊管理學系 |
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授課教師 |
盧信銘 |
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課號 |
IM5056 |
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課程識別碼 |
725 U3670 |
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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 |
星期四2,3,4(9:10~12:10) |
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上課地點 |
管二305 |
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備註 |
商業資料分析學分學程課程
總人數上限：70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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為確保您我的權利,請尊重智慧財產權及不得非法影印
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課程概述 |
本課除第一次上課、學期中一至二次期末報告計劃書討論、與期末報告為實體授課外，其餘課程採線上教學。
考量助教的負擔，加選的名額分配如下 (as of 2023/8/15)：
* 資管系所 7人
* 商資分析學程 7人
* 經濟系 7人
* 不分系所 7人
請至以下連結登記加選。第一次實體上課時會以抽籤的方式決定可以加選的同學：
https://forms.gle/6pVmuaiW5xj7jAgZA
中簽名單 (2023/9/7): https://docs.google.com/spreadsheets/d/1y7uUEZUHX5eQYGgZLAyt0ZR_OFkPOANOrMTMS-NK56k/edit?usp=sharing
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. |
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課程目標 |
Omitted. |
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課程要求 |
本課程對程式能力有一些要求，如果你不會寫程式，請不要修這門課。助教不會教你寫程式、不會幫你寫程式、不會幫你安裝Jupyter Lab或其他工具。具體的程式能力的要求是：
‧ 本課程會使用Python + Jupyter Lab 做為主要工具，修課學生須具備基本的Python Programming知識，例如Jupyter Lab安裝與操作、Import 與使用Module、使用Pandas與Numpy操作與分析資料等。
‧ 自己延伸學習在課堂與作業中會使用的其他Module。
*Grade Distribution*
The course grades will be determined by the following percentages:
Homework (Python-based) (5-6 Assignments) 60%
Attendance, participation & quizzes 10%
Final Project (Presentation) 30%
Total 100%
*Computational Tools*
Students are required to use Python 3 (Version >=3.7) (with scikit-learn, pandas, matplotlib, numpy, pytorch, etc.) to solve homework assignments.
*Homework*
There are at least five graded assignments. Unless otherwise stated, students must organize their code and results using Jupyter Lab (with Python 3; Version >=3.8) and submit their *.IPYNB file together with the exported HTML file to NTUCOOL. Other file formats are not accepted and will result in a zero score for the homework. You will have two weeks to finish each homework assignment (Due time: 12:30 pm Thursday). Late submissions will not be accepted. Homework assignments play a critical role in the learning process, and students are expected to spend a significant amount of time in solving homework problems. Note that plagiarism will result in a 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. |
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預期每週課後學習時數 |
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Office Hours |
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指定閱讀 |
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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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