Machine Learning
本課程是由 國立陽明交通大學應用數學系提供。
"Google's always used machine learning. In all the areas we applied it to, speech recognition, then image understanding, and eventually language understanding, we saw tremendous improvements."
John Giannandrea, then VP of Engineering, Google
In the last decade, machine learning has been applied to many real world problems successfully. It is considered as the most essential and fundmental knowledge for a data scientist. We introduce core concept of machine learning and several useful learning methods including linear models, nonlinear models, kernel methods, dimension reduction, unsupervised learning (Clustering) and deep learning. Also some special topics and applications will be discussed.
Textbook：
Deep Learning, Ian Goodfellow, Yoshua Bengio and Aaron Courville, 2016,The MIT Press
For perfect learning results, please buy textbooks!
| Instructor(s) | Department of Applied Mathematics Prof. Yuh-Jye Lee |
|---|---|
| Course Credits | 3 Credits |
| Academic Year | 105 Academic Year |
| Level | Master student |
| Prior Knowledge | Mathematical analysis, Numerical Methods, Linear Algebra, Probability, Programming skills |
| Related Resources | Course Video Course Syllabus Course Calendar |
課程目標
"Google's always used machine learning. In all the areas we applied it to, speech recognition, then image understanding, and eventually language understanding, we saw tremendous improvements."
John Giannandrea, then VP of Engineering, Google
In the last decade, machine learning has been applied to many real world problems successfully. It is considered as the most essential and fundmental knowledge for a data scientist. We introduce core concept of machine learning and several useful learning methods including linear models, nonlinear models, kernel methods, dimension reduction, unsupervised learning (Clustering) and deep learning. Also some special topics and applications will be discussed.
課程章節
| 主題內容 |
| Introduction to Machine Learning |
| Machine Learning: Overview |
| Mathematical Background |
| Three Fundamental Learning Algorithms 1. Naive Bayes Algorithm 2. k-Nearest Neighbor 3. Perceptron Algorithm |
| Evaluating the Learning Models |
| Learning Theory |
| Optimization |
| Support Vector Machine(SVM) |
| Sequential Minimal Optimization (SMO) |
| Support Vector Machine(SVM) |
| Anomaly Detection via Online Over Sampling Principal Component Analysis |
| Nonlinear Dimension Reduction with Kernel Sliced Inverse Regression |
| Clustering and EM Algorithm |
| Online Nonlinear Support Vector Machine for Large-Scale Classification |
課程書目
Deep Learning, Ian Goodfellow, Yoshua Bengio and Aaron Courville, 2016.
閱讀書目
1. Wing, Jeannette M. Computational thinking." Communications of the ACM 49.3 (2006): 33-35.
2. Domingos, Pedro. A few useful things to know about machine learn- ing." Communications of the ACM 55.10 (2012): 78-87.
3. Dhar, Vasant. "Data science and prediction." Communications of the ACM 56.12 (2013): 64-73.
閱讀書目
1. Ethem Alpaydin, Introduction to Machine Learning, 3rd Edition, 2014, ISBN: 978-0-262-028189
http://www.cmpe.boun.edu.tr/ethem/i2ml3e/
2. Abu-Mostafa, Yaser S., Malik Magdon-Ismail, and Hsuan-Tien Lin. Learning from data. Berlin, Germany: AMLBook, 2012.
3. Friedman, Jerome, Trevor Hastie, and Robert Tibshirani. The ele- ments of statistical learning. Vol. 1. Springer, Berlin: Springer series in statistics, 2001.
4. Witten, Ian H., and Eibe Frank. Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, 2005.
5. Vapnik, Vladimir Naumovich, and Vlamimir Vapnik. Statistical learn- ing theory. Vol. 1. New York: Wiley, 1998.
6. Mitchell, Tom M. Machine learning. McGraw Hill,1997.
評分標準
| 項目 | 百分比 |
| 作業 | 30% |
| 期末考 | 40% |
| 期末專題：A Kaggle Competition | 30% |
本課程行事曆提供課程進度與考試資訊參考。
| 主題內容 |
| Introduction to Machine Learning |
| Machine Learning: Overview |
| Mathematical Background |
| Three Fundamental Learning Algorithms 1. Naive Bayes Algorithm 2. k-Nearest Neighbor 3. Perceptron Algorithm |
| Evaluating the Learning Models |
| Learning Theory |
| Optimization |
| Support Vector Machine(SVM) |
| Sequential Minimal Optimization (SMO) |
| Support Vector Machine(SVM) |
| Anomaly Detection via Online Over Sampling Principal Component Analysis |
| Nonlinear Dimension Reduction with Kernel Sliced Inverse Regression |
| Clustering and EM Algorithm |
| Online Nonlinear Support Vector Machine for Large-Scale Classification |
