5701 - 機器學習 英授 Taught in English

Machine Learning

教育目標 Course Target

機器學習是透過演算法,使用歷史資料做訓練以建立模型,並依此模型對於新的資料進行預測。本課程涵蓋機器學習的基礎理論、演算法、以及應用,探討什麼是機器學習?機器可能學習嗎?如何學習?如何做到較好的學習?讓同學了解機器學習的理論與實務。

Machine learning uses algorithms to train historical data to build models and predict new data based on this model. This course covers the basic theories, algorithms, and applications of machine learning, and explores what is machine learning? Can the machine be learned? How to learn? How to achieve better learning? Let students understand the theory and practice of machine learning.

課程概述 Course Description

Machine learning is the science of data analysis that automates a massive number of models building. Its process uses data to iteratively detect patterns and adjust models accordingly, and enables computers to learn without explicitly programmed. This course introduces some important concepts and algorithms of machine learning from both theoretical and practical perspective. The topics include, but not limited to: (1) Supervised learning (Linear Models for Regression and Classification, Kernel Smoothing Methods, Decision Trees, Support Vector Machines, and Neural Networks). (2) Unsupervised learning (Association Rules and Cluster Analysis). (3) Ensemble learning (Bagging, Boosting, Random Forests). (4) Others (MCMC, Optimization Integration).

Machine learning is the science of data analysis that automatically a massive number of models building. Its process uses data to iteratively detect patterns and adjust models accordingly, and enables computers to learn without explicitly programmed. This course introduces some important concepts and algorithms of machine learning from both theoretical and practical perspective. The topics include, but not limited to: (1) Supervised learning (Linear Models for Regression and Classification, Kernel Smoothing Methods, Decision Trees, Support Vector Machines, and Neural Networks). (2) Unsupervised learning (Association Rules and Cluster Analysis). (3) Ensemble learning (Bagging, Boosting, Random Forests). (4) Others (MCMC, Optimization Integration).

參考書目 Reference Books

[1] Yaser S. Abu-Mostafa, Malik Magdon-Ismail and Hsuan-Tien Lin, Learning From Data, AMLbook.com, 2012.
[2] Ethem Alpaydın, Introduction to Machine Learning, 2nd Ed. The MIT Press Cambridge, 2010.
[3] An Introduction to Machine Learning Theory and Its Applications: A Visual Tutorial with Examples, by Nick McCrea.
[4] Deep Reinforcement Learning, David Silver, Google DeepMind, 2017 (http://www.iclr.cc/lib/exe/fetch.php?media=iclr2015:silver-iclr2015.pdf)
[5] Reinforcement Learning: An Introduction, by Richard S. Sutton,‎ Andrew G. Barto, A Bradford Book, 2017

[1] Yaser S. Abu-Mostafa, Malik Magdon-Ismail and Hsuan-Tien Lin, Learning From Data, AMLbook.com, 2012.
[2] Ethem Alpaydın, Introduction to Machine Learning, 2nd Ed. The MIT Press Cambridge, 2010.
[3] An Introduction to Machine Learning Theory and Its Applications: A Visual Tutorial with Examples, by Nick McCrea.
[4] Deep Reinforcement Learning, David Silver, Google DeepMind, 2017 (http://www.iclr.cc/lib/exe/fetch.php?media=iclr2015:silver-iclr2015.pdf)
[5] Reinforcement Learning: An Introduction, by Richard S. Sutton,‎ Andrew G. Barto, A Bradford Book, 2017

評分方式 Grading

評分項目
Grading Method
配分比例
Percentage
說明
Description
期中考
Midterm exam
30 筆試
期末專案
Final period project
30 分組專案
作業
Action
30 回家作業
出席
Attend
10 出席

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課程資訊 Course Information

基本資料 Basic Information

  • 課程代碼 Course Code: 5701
  • 學分 Credit: 0-3
  • 上課時間 Course Time:
    Friday/6,7,8[C101]
  • 授課教師 Teacher:
    林祝興/陳隆彬
  • 修課班級 Class:
    資工系4,碩1,2
  • 選課備註 Memo:
    大四可選
選課狀態 Enrollment Status

目前選課人數 Current Enrollment: 45 人

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