Semester	Spring Semester, 2020
Department	Social Networks and Human-Centered Computing, First Year Social Networks and Human-Centered Computing, Second Year
Course Name	Advanced Algorithms
Instructor
Credit	3.0
Course Type	Elective
Prerequisite

Course Objective

Course Description

Course Schedule

Week	Date	Topics/Brief Description	Subtopics/Detail Descriptions/Examples	Lecturers
1		Databases: An Overview	N/A
2		Introduction to Data Mining	N/A
3		Data Classification: Overview	N/A
4		Standard Optimization Algorithms	The outline of the optimization couse: basic idea of optimization, convex optimization, lagrangial method for optimization method, and gradient descent methods.
5		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.
6		Kernel Methods	N/A
7		Nonstandard Optimization Algorithms (GA, Random Forest, and others)	N/A
8		Review Week
9		Midterm Exam
10		Hidden Markov Models (I)	N/A
11		Hidden Markov Models (II)	N/A
12		Graphical Models (I)	N/A
13		Graphical Models (II)	N/A
14		Conditional Random Fields	N/A
15		MapReduce in Cloud Computing	N/A
16		Network Analysis	N/A
17		Review Week
18		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)

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