104年度下學期－機器學習
機器學習
MACHINE LEARNING
ICE535
課程類別：講授類
必選修：選修
系所：通訊工程研究所碩士班
授課教師：溫朝凱
學分：3
課程大鋼：
Data analysis methods in machine learning are widely considered as a useful tool in recent industry and science. With the growth of the Web and improvements in data collection technology in science, the magnitude and complexity of these analysis tasks also rapid increase. This trend is driving the need for scalable, parallel and online algorithms and models which can handle such “Big Data”. This course will provide a broad foundation for this timely challenge.
課程目標：
1. Modeling Techniques: linear models, graphical models, matrix and tensor factorizations, clustering, and latent factor models
2. Algorithmic Topics: sketching, fast n-body problems, random projections and hashing, large-scale online learning, and parallel learning
3. Computational Techniques: a basic foundation in large-scale programming, ranging from the basic "parfor" to parallel abstractions
授課方式：
Lecture
評分方式：
1.Quizzes：30%
2.Midterm exam：30%
3.Final exam：40%
每週課程期預定進度：
|
週次 |
日期 |
授課內容及主題 |
|
1 |
2016/02/22~2016/02/28 |
Introduction |
|
2 |
2016/02/29~2016/03/06 |
Overview of Supervised Learning |
|
3 |
2016/03/07~2016/03/13 |
Linear Methods for Regression |
|
4 |
2016/03/14~2016/03/20 |
Linear Methods for Classification |
|
5 |
2016/03/21~2016/03/27 |
Basis Expansions and Regularization |
|
6 |
2016/03/28~2016/04/03 |
Kernel Smoothing Methods |
|
7 |
2016/04/04~2016/04/10 |
Model Assessment and Selection |
|
8 |
2016/04/11~2016/04/17 |
Model Inference and Averaging |
|
9 |
2016/04/18~2016/04/24 |
Midle Exam |
|
10 |
2016/04/25~2016/05/01 |
Additive Models, Trees, and Related Methods |
|
11 |
2016/05/02~2016/05/08 |
Additive Models, Trees, and Related Methods |
|
12 |
2016/05/09~2016/05/15 |
Boosting and Additive Trees |
|
13 |
2016/05/16~2016/05/22 |
Neural Networks |
|
14 |
2016/05/23~2016/05/29 |
Support Vector Machines and Flexible Discriminants |
|
15 |
2016/05/30~2016/06/05 |
Prototype Methods and Nearest-Neighbors |
|
16 |
2016/06/06~2016/06/12 |
Unsupervised Learning |
|
17 |
2016/06/13~2016/06/19 |
Random Forests |
|
18 |
2016/06/20~2016/06/26 |
Final Exam |
課程討論時間：
時段1:
時間：星期一12:00~ 14:00
地點：工9009
時段2：
時間：星期二12:00~ 14:00
地點：工9009
