106年第2學期-5701 機器學習 課程資訊

課程分享

選課分析

本課程名額為 70人,已有45人選讀,尚餘名額25人。

評分方式

評分項目 配分比例 說明
期中考 30 筆試
期末專案 30 分組專案
作業 30 回家作業
出席 10 出席

授課教師

林祝興 陳隆彬

教育目標

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。

課程概述

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).

課程資訊

參考書目

[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