一、 課程說明 (Course Description) 
<br>
<br>This course presents a unified treatment of machine learning problems and 
<br>solutions. Basically, machine learning is about programming computers to 
<br>optimize a performance criterion using example data or past experience. Consider 
<br>the recognition of spoken speech—that is, converting the acoustic speech signal 
<br>to an ASCII text; humans can do this task seemingly without any difficulty, but 
<br>we are unable to explain how we do it. In machine learning, the approach is to 
<br>collect a large collection of sample utterances from different people and learn 
<br>to map these to words. Another example is that developers of a web site (e.g., 
<br>YouTube) usually collect user behaviors (e.g., mouse clicks), apply machine 
<br>learning to analyze the preference of individual users, and recommend items 
<br>(e.g., clips) that may be interesting to these users.
<br>
<br>The topics to be discussed have their bases in different fields, including 
<br>statistics, pattern recognition, neural networks, artificial intelligence, 
<br>signal processing, control, and data mining. All algorithms are explained so 
<br>that the student can easily move from the equations to a computer program. 
<br>
<br>This course is intended for senior undergraduate and graduate students who have 
<br>proper understanding of computer programming, probability, calculus, and linear 
<br>algebra. 
<br>
<br>二、 指定用書 (Textbook)
<br>
<br>[1].	Lecture Notes
<br>[2].	Introduction to Machine Learning, 2ed,by Ethem Alpaydin, ISBN: 
<br>026201243X 
<br>
<br>三、 參考書籍 (References) 
<br>
<br>[1].	Pattern Recognition and Machine Learning, by Christopher M. Bishop, 
<br>ISBN: 0387310738
<br>[2].	Data Mining: Practical Machine Learning Tools and Techniques, 2ed, by 
<br>Ian H. Witten, et al., ISBN: 0120884070
<br>
<br>四、 教學方式 (Teaching Method) 
<br>
<br>Lecture 
<br>
<br>五、 教學進度 (Syllabus) 
<br>
<br>1.	Introduction to machine learning
<br>2.	A review of the basics: linear algebra, multivariate calculus, and 
<br>probability
<br>3.	Supervise learning
<br>4.	Bayesian decision theory
<br>5.	Parametric methods
<br>6.	Multivariate methods
<br>7.	Dimensionality reduction
<br>8.	Clustering
<br>9.	Nonparametric methods
<br>10.	Linear discrimination
<br>11.	Kernel machines
<br>12.	Hidden Markov models
<br>13.	Graphical models
<br>14.	Combining multiple leaners
<br>15.	Metrics and experiments 
<br>
<br>六、 成績考核 (Evaluation) 
<br>Midterm exam: 30% 
<br>Final exam: 40% 
<br>Assignments 30% 
<br>