SemesterSpring Semester, 2018
DepartmentSocial Networks and Human-Centered Computing, First Year Social Networks and Human-Centered Computing, Second Year
Course NameAdvanced Algorithms
Instructor
Credit3.0
Course TypeElective
Prerequisite
Course Objective
Course Description
Course Schedule

































































































































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



Teaching Methods
Teaching Assistant
Requirement/Grading

Midterm exam 50%. Final exam 50%.


Textbook & Reference

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)




  1. Learning Pattern Classification (Duda, Harg, and Stork, 2001)

  2. 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/ )


Urls about Course
Attachment