| 課程編碼 Course Code | 中文課程名稱 Course Name (Chinese) | 英文課程名稱 Course Name (English) | 總學分數 Credits | 總時數 Hours |
|---|---|---|---|---|
| 5905189 | 機器學習 | Machine Learning | 3.0 | 3 |
| 中文概述 Chinese Description | 本課程以深入淺出之方式，介紹與機器學習之相關技術及演算法，目標是修習本課程之學生具有閱讀機器學習相關文獻之基本知識，並能從事相關之實務或電腦模擬實驗之工作，因此本課程著重在廣泛介紹各類之演算法之應用，而非深究少數演算法之深奧數學原理．本課程涵蓋之演算法，包含：最佳化基本知識，隨機最佳化演算法，隨機搜尋，決策樹，隨機森林，類神經網路及深度學習相關演算法，基因演算法，貝氏定理及應用，高斯混合模型，隱藏式馬可夫鍊(及訓練演算法)，支援向量機，線性及邏輯回歸，分群方法，降維方法，提升方法，決策融合技巧，及增強式學習等。 | |||
| 英文概述 English Description | This course covers most of the techniques and algorithms related to the current trend in the machine learning field. The goal of the course is that students complete this course can read and understand the literature in the machine learning field, and can conduct computer simulations for experiments or apply the techniques to practical problems. Therefore, the course intends to cover a broad range of algorithms with emphasis on applications, rather than to cover only a few algorithms with extensive mathematical treatments. The algorithms covered in this course include: foundations of optimizations, random search, decision tree, random decision forests, neural networks and deep learning, genetic algorithm, Bayes' theorem and its applications, Gauss mixture model, hidden Markov model, support vector machine, linear and logistic regressions, clustering algorithms, dimension reduction techniques, boosting methods, decision level fusion techniques, and reinforcement learning. | |||
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