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.
- HW1: 2.2, 2.10, 2.16 due on March 3.
- HW2: 2.24, 3.1, 3.28 due on March 17.
- HW3: 4.1(a) and (d), 4.21(a), and 4.43(b) due on April 7
- HW4: 5.1(a)-(c), 5.22(a)-(c), 5.26, due on April 21
- HW5: Redo problem 6 of the exam, but consider logistic regression. You are required to use the two ways described in the solution and check their possible connections. Due on May 12.
- HW6: large-scale logistic regression implementation. See slides. Due on June 9.
Exams
You can bring notes and the textbook.
Other books or
electronic devices are not allowed.
- Midterm 1: March 17
- Midterm 2: April 21
- Final: June 16.
Sample exams in the past: exam1,
exam2, exam3.
For midterms, discussions will be in the following week. For the Final exam, it will be on June 19, 12pm (room 111, CSIE building).
Grading
30% homework, 70% Exam. (tentative)
Some (usually 10%) may fail if they don't work hard.
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