目前產業界、科研領域都需要AI人才。本課目標提供愛好AI同學學會機器學習技術，能夠快速、系統化掌握人工智慧技術的最新趨勢，並徹底瞭解人工智慧如何運作。
課程會先總攬機器學習的理論，包括分類、定義問題與方法，接著進入機器學習的核心神經網路的重要概念，搭配手把手實例與自行練習，不僅對於理論深度掌握，並且實作類神經網路。
課程中將介紹企業實際案例，並邀請業界進行實務及專題分享。期末成果舉行發表會，邀請企業執行長、技術主管擔任評審，做為同學就業提前部屬。
對象:大學部資工系(非資工系同學亦可)。需要有程式基礎，python 更佳。歡迎想要一次把AI、機器學習學好、學滿的同學選修。
教師:蔡清欉；合作教師賴泳瑄(黑瘋科技) 、陳仕偉。三位都具備產業實務，課程也邀請非常受學生歡迎名師前來客座授課。
Currently, AI talents are needed in the industry and scientific research fields. This course aims to provide AI AI student machine learning technology, which can quickly and systematically master the latest trends of artificial intelligence technology, and to understand how artificial intelligence works.
The course will first summarize the theory of machine learning, including classification, definitional problems and methods, and then enter the core neural network of machine learning, and combine it with hand-held examples and self-practice. It not only has a deep understanding of theory, but also implements a neural network. .
In the course, the actual enterprise cases will be introduced and the industry will be invited to share practices and topics. The final results will be presented, and the company executive and technical supervisors will be invited to review and become the early department of the students to study.
Object: University Department of Qualifications (can also be students of non-working departments). It requires programming basics, python is better. We welcome students who want to learn AI and machine well and have a good learning experience at one time.
Teacher: Cai Qing-shang; cooperative teachers Feng Yongxuan (Black Crazy Technology), Chen Shiwei. All three have professional skills, and the course also invites highly welcomed students to come to the guest to teach the course.
Pattern Recognition and Machine learning, bishop springer
Deep learning, Ian Goodfellow, Yoshua Bengio and Aaron Courville, 2016
深度學習快速入門 使用tensorflow/ 博碩文化 / Giancarlo Zaccone 著 傅運文翻譯
Pattern Recognition and Machine Learning, bishop springer
Deep learning, Ian Goodfellow, Yoshua Bengio and Aaron Courville, 2016
In-depth learning Quick entry using tensorflow/Boyan Culture/ by Giancarlo Zaccone Translation of Fu Changwen
| 評分項目 Grading Method | 配分比例 Grading percentage | 說明 Description |
|---|---|---|
| 期中考期中考 Midterm exam |
20 | |
| 期末考期末考 Final exam |
30 | |
| 作業作業 Action |
40 | |
| 課堂參與 (小考)課堂參與 (小考) Class Participation (Small Exam) |
10 |
