1116 - 機器學習
Machine Learning
教育目標 Course Target
本課程為人工智慧的相關課程，首先介紹機器學習的基礎概念，如:監督式學習、與非監督學習的相關技術，並透過實例介紹機器學習基礎的model與核心概念與應用。當學生具備有基本機器學習基礎後，再介紹類神經網路及深度學習，包含如何訓練及優化類神經網路(NN)、深度神經網路(DNN)與卷積神經網路(CNN)…等深度學習模型，最後介紹強化式學習。透過TensorFlow /Keras所提供的模組與實務專案讓同學動手實作。
This course is related to artificial intelligence. It first introduces the basic concepts of machine learning, such as supervised learning, and related technologies of unsupervised learning. It also introduces basic models and core concepts and applications of machine learning through examples. After students have a basic foundation in machine learning, they will then introduce neural networks and deep learning, including how to train and optimize deep learning models such as neural networks (NN), deep neural networks (DNN), and convolutional neural networks (CNN). Finally, reinforcement learning will be introduced. Let students practice through the modules and practical projects provided by TensorFlow/Keras.
課程概述 Course Description
本課程將介紹機器學習相關演算法，並說明如何透過Python程式語言實作機器學習演算法。
This course will introduce machine learning related algorithms and explain how to implement machine learning algorithms through the Python programming language.
參考書目 Reference Books
機器學習：Python程式實作，張元翔，高立圖書
Machine Learning: Python Program Implementation, Zhang Yuanxiang, Gao Li Books
評分方式 Grading
| 評分項目 Grading Method |
配分比例 Percentage |
說明 Description |
|---|---|---|
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期中考 midterm exam |
25 | |
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期末考 final exam |
25 | |
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上課作業 Classwork |
40 | |
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出席狀況與其他 Attendance and other |
10 |
授課大綱 Course Plan
點擊下方連結查看詳細授課大綱
Click the link below to view the detailed course plan
相似課程 Related Courses
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課程資訊 Course Information
基本資料 Basic Information
- 課程代碼 Course Code: 1116
- 學分 Credit: 0-3
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上課時間 Course Time:Tuesday/7,8,9[HT109]
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授課教師 Teacher:陳昱仁
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修課班級 Class:電機系3,4
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選課備註 Memo:上課教室：HT109。
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