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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 |
108-2 |
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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 |
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 |
管一402 |
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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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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 (Python-based) (5-6 Homework) 50%
Attendance, participation & quizzes 10%
Group Project 1 (Based on relative prediction performance) 20%
Group Project 2 (Presentation) 20% |
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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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