Course Information
Course title
機器學習特論
Topics in Machine Learning 
Semester
105-2 
Designated for
COLLEGE OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE  GRADUATE INSTITUTE OF NETWORKING AND MULTIMEDIA  
Instructor
林智仁 
Curriculum Number
CSIE7435 
Curriculum Identity Number
922EU3940 
Class
 
Credits
3.0 
Full/Half
Yr.
Half 
Required/
Elective
Elective 
Time
Tuesday 2,3,4(9:10~12:10) 
Room
資101 
Remarks
本課程以英語授課。
The upper limit of the number of students: 80. 
Ceiba Web Server
http://ceiba.ntu.edu.tw/1052CSIE7435_ 
Course introduction video
 
Table of Core Capabilities and Curriculum Planning
Table of Core Capabilities and Curriculum Planning
Course Syllabus
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Course Description

Optimization techniques are used in all kinds of machine learning problems because in general we would like to minimize the testing error. This course will contain two parts. The first part focuses on convex optimization techniques. We discuss methods for least-squares, linear and quadratic programs, semidefinite programming, and others.
We also touch theory behind these methods (e.g., optimality conditions and duality theory). In the second part of this course we will investigate how optimization techniques are applied to various machine learning problems (e.g., SVM, maximum entropy, conditional random fields, sparse reconstruction for signal processing applications). We further discuss that for different machine learning applications how to choose right optimization methods. 

Course Objective
learn how to use optimization techniques for solving machine learning problems. 
Course Requirement
待補 
Student Workload (expected study time outside of class per week)
 
Office Hours
 
Designated reading
待補 
References
Convex optimization 
Grading
   
Progress
Week
Date
Topic