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 |
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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 |
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Table of Core Capabilities and Curriculum Planning |
Table of Core Capabilities and Curriculum Planning |
Course Syllabus
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Please respect the intellectual property rights of others and do not copy any of the course information without permission
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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) |
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Office Hours |
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Designated reading |
待補 |
References |
Convex optimization |
Grading |
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