Optimization and Machine Learning
Course Outline
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.
- Convex set, Convex function
- Linear, quadratic programming
- Convex optimization
- Duality
- Unconstrained minimization
- Equality constrained minimization
- SVM
- Maximum entropy, CRF
- Applications
Homework
Once every two weeks. Please write your homework/reports in English.
For late homework, the score will be exponentially decreased. See
FAQ about how to submit your
homework.
All HW questions can be found in the textbook.
- HW1: 2.1, 2.5, 2.7, due on March 7
- HW2: 2.17(c), 2.24, 3.3, due on March 21
Exams
You can bring notes and the textbook.
Other books or
electronic devices are not allowed.
- Midterm 1: March 28 (week 6)
- Midterm 2: May 23 (week 14)
- Final: June 20 (week 18)
Sorry that the 2nd midterm is a bit late, but this is due to
my conference schedule.
Sample exams in the past: exam1,
exam2, exam3.
For midterms, discussions will be in the following week. For the Final exam, it will be at ?? on ?? (room ??, CSIE building).
Grading
30% homework, 70% Exam. (tentative)
Some (usually 10%) may fail if they don't work hard.
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