◎系所教育目標：
為配合國家建設及產業發展之需要，本系以培育中高級資訊科技人才為目的。在教學理念上除了注重理論的探討之外並強調實際動手的能力，以期培育出具有深厚學識基礎並能實際應用的資訊科技人才。在專業必修中涵蓋基礎理論、電腦硬體、作業系統、資料結構及計算機網路等方面，並有畢業專題製作，使學生紮實基礎，同時課程包含四個專業學程，兼顧學術及實務之分流與訓練。分別為一：軟體工程及知識工程學程、二：互動多媒體學程、三：網路及資訊安全學程、四：資訊系統開發實務學程，以期作為日後升學就業的準備。 |
| ◎核心能力 | 關聯性 |
| 1.應用數理邏輯推理之能力 | 3 關聯性中等 |
| 2.具備資訊軟體專業之能力 | 3 關聯性中等 |
| 3.具備資訊硬體專業之能力 | 2 關聯性稍弱 |
| 4.發掘、分析及解決問題之能力 | 4 關聯性稍強 |
| 5.現代資訊發展工具之使用與熟悉資訊應用系統之能力 | 3 關聯性中等 |
| 6.資訊軟體或硬體創新設計與實作之能力 | 2 關聯性稍弱 |
| 7.有效溝通與團隊合作之能力 | 2 關聯性稍弱 |
| 8.培養人文素養、專業倫理責任、社會關懷與生活技能之能力 | 2 關聯性稍弱 |
◎本學科學習目標：
This course gives an overview of many concepts, techniques, and algorithms in machine learning, beginning with topics such as classification and linear regression and ending up with more recent topics such as boosting, support vector machines, and Bayesian networks. The course will give the student the ideas and intuition behind modern machine learning methods as well as the underlying theory of these methods. The course will also discuss recent applications of machine learning, such as data mining, object detection, speech recognition, and text data processing. |
| ◎教學進度： |
| 週次 | 主題 | 教學內容 | 教學方法 |
01
02/18 | Course introduction | Course introduction | 講授、討論。 |
02
02/25 | Introduction to machine learning | classification, learning, features, and applications | 講授、討論。 |
03
03/04 | Probability | Probability Densities | 講授、討論。 |
04
03/11 | Supervised learning | Supervised learning | 作業/習題演練、問題教學法、講授、討論。 |
05
03/18 | Bayesian decision theory | Bayesian theory | 作業/習題演練、問題教學法、講授、討論。 |
06
03/25 | Parametric methods | Parametric methods | 作業/習題演練、問題教學法、講授、討論。 |
07
04/01 | School anniversary deferred holiday | School anniversary deferred holiday | Suspend class。 |
08
04/08 | Dimensionality reduction | Dimensionality reduction | 作業/習題演練、問題教學法、講授、討論。 |
09
04/15 | Mid-term exam | Mid-term exam | Mid-term exam。 |
10
04/22 | Clustering | Clustering, k-means, hierarchical agglomeration | 作業/習題演練、問題教學法、講授、討論。 |
11
04/29 | Nonparametric methods | K nearest neighbor | 作業/習題演練、問題教學法、講授、討論。 |
12
05/06 | Decision trees | The theory of decision trees | 作業/習題演練、問題教學法、講授、討論。 |
13
05/13 | Linear discrimination | Linear discrimination | 作業/習題演練、問題教學法、講授、討論。 |
14
05/20 | Kernel Machines | Kernel Machines | 作業/習題演練、問題教學法、講授、討論。 |
15
05/27 | Graphical models | Graphical models | 作業/習題演練、問題教學法、講授、討論。 |
16
06/03 | Ensemble methods | Ensemble methods, such as bagging, boosting | 作業/習題演練、問題教學法、講授、討論。 |
17
06/10 | Design and analysis of machine learning experiments | Design and analysis of machine learning experiments | 作業/習題演練、問題教學法、講授、討論。 |
18
06/17 | Final exam | Final exam | Final exam。 |
◎課程要求：
Class participation:
All students need to participate in discussions on the instructor’s lectures. Levels of participation will be evaluated based on each student's contribution to lectures and class discussions, not just on class attendance.
Assignments:
Assignments, homework, and hands-on reports must be turned in on time when they are due. Unfinished assignments and homework turned in on time will be graded; however, assignments not turned in on the due date will NOT be accepted. |
◎成績考核
課堂參與討論5% : including course attendance
小考15%
期中考30%
期末考30%
作業/習題演練20%
|
◎參考書目與學習資源
• Machine Learning: a Probabilistic Perspective, Kevin Murphy, MIT Press, 2013.
• Pattern Recognition and Machine Learning, Christopher Bishop, Springer, 2007.
• Machine Learning, Tom Mitchell, McGraw-Hill, 1997.
• The Elements of Statistical Learning, Friedman, Tibshirani, Hastie, Springer, 2001. |
| ◎教材講義 請改以帳號登入校務系統選擇全校課程查詢方能查看教材講義 |