Week | Date | Topics/Brief Description | Subtopics/Detail Descriptions/Examples | Lecturers | 1 | 2018/02/23 | Databases: An Overview | N/A | Dr. Chih-Ya Shen | 2 | 2018/03/02 | Introduction to Data Mining | N/A | Dr. Chih-Ya Shen | 3 | 2018/03/09 | Data Classification: Overview | N/A | Dr. Yuh-Jye Lee | 4 | 2018/03/16 | Standard Optimization Algorithms | The outline of the optimization couse: basic idea of optimization, convex optimization, lagrangial method for optimization method, and gradient descent methods. | Dr. Wen-Liang Hwang | 5 | 2018/03/23
| Support Vector Machines and Large Margin | The outline of support vector machine: basic principles for maching learning, the support vector machine is a convex optimization method. So, I can use the results of week 1 in week 2. | Dr. Wen-Liang Hwang | 6 | 2018/03/30
| Kernel Methods | N/A | Dr. Wen-Liang Hwang | 7 | 2018/04/06 | Nonstandard Optimization Algorithms (GA, Random Forest, and others) | N/A | Dr. Huai-Kuang Tsai | 8 | 2018/03/13 | Review Week | 9 | 2018/04/20 | Midterm Exam | 10 |
2018/04/27 | Hidden Markov Models (I) | N/A | Dr. Yu Tsao | 11 | 2018/05/04 | Hidden Markov Models (II) | N/A | Dr. Yu Tsao | 12 | 2018/05/11 | Graphical Models (I) | N/A | Dr. Hsing-Kuo Pao | 13 | 2018/05/18 | Graphical Models (II) | N/A | Dr. Hsing-Kuo Pao | 14 | 2018/05/25 | Conditional Random Fields | N/A | Dr. Richard Tzong-Han Tsai | 15 | 2018/06/01 | MapReduce in Cloud Computing | N/A | Dr. Yu-Rong Chang | 16 | 2018/06/08 | Network Analysis | N/A | Dr. Huan-Cheng Hwang | 17 | 2018/06/15 | Review Week | 18 | 2018/06/22 | Final Exam |
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References: (reserved in the library of the Institute of Information Science)
1. First Course in Database Systems (3rd Edition, Ullman and Widom, 2007)
2. Learning from Data- A Short Course (Abu-Mostafa, Magdon-Ismail, Lin, 2012)
- Learning Pattern Classification (Duda, Harg, and Stork, 2001)
- An Introduction to Support Vector Machines and Other Kernel-based Learning Methods (Cristianini and Shawe-Taylor, 2000)
5. Convex optimization (Boyd and Vandenberghe, 2004; book and lecture slides available at http://www.stanford.edu/~boyd/cvxbook/ )
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