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.
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).
[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
| 評分項目 Grading Method | 配分比例 Grading percentage | 說明 Description |
|---|---|---|
| 期中考期中考 Midterm exam |
30 | 筆試 |
| 期末專案期末專案 Final period project |
30 | 分組專案 |
| 作業 作業 Action |
30 | 回家作業 |
| 出席出席 Attend |
10 | 出席 |
