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Instructor:
Dan Gildea office hours MW 4-5pm
Time: M/W 10:25-11:40, CSB 703
Homework
Required text: Boyd and Vanderberghe, Convex Optimization
In addition to this text, we will cover additional numerical
methods taken from Nocedal and Wright and Numerical Recipes. The
second half of the course will focus on applications of
optimization in the context of machine learning.
| W 9/3 | Intro | Boyd ch 1 |
| M 9/8 | Convex Sets | Boyd ch 2 |
| W 9/10 | Convex Functions | Boyd ch 3 |
| M 9/15 | Convex Optimization Problems | Boyd ch 4 |
| W 9/17 | Convex Optimization Problems |
| M 9/22 | Duality | Boyd ch 5 |
| W 9/24 | Duality | Boyd ch 5 |
| M 9/29 | Approximation and Statistical Estimation | Boyd ch 6, 7 |
| W 10/1 | Geometric Problems | Boyd ch 8 |
| M 10/6 | Unconstrained Minimization | Boyd ch 9 |
| W 10/9 | Equality Constrained Minimization | Boyd ch 10 |
| W 10/15 | Interior Point Methods | Boyd ch 11 |
| M 10/20 | Interior Point Methods | Boyd ch 11 |
| W 10/22 | BFGS | Nocedal ch 8 |
| M 10/27 | Simulated Annealing | Kirkpatrick |
| W 10/29 | Dongarra Seminar | |
| M 11/3 | Deterministic Annealing | Rose |
| W 11/5 | Numerical Methods | Press 10.4 - 10.6 |
| M 11/10 | Integer Programming | Nemhauser ch II.4 |
| W 11/12 | Shujie Chen | Haider |
| M 11/17 | Linfeng Song | MERT, MIRA, perceptron, rampion |
| W 11/19 | Yina Jin | x-means |
| M 11/24 | Xiaocheng Peng | spectral learning: Hsu Carreras |
| W 11/26 | Dual Decomposition for Parsing | Rush et al. Koo et al. |
| M 12/1 | AdaGrad | Duchi |
| W 12/3 | Graphical Models | Wainwright and Jordan ch 3 |
| M 12/8 | Graphical Models cont'd | Wainwright and Jordan ch 4 |
| W 12/10 | Project Presentations |
Projects due: 5pm Friday 12/12
Grading
Students will have written assignments, present one lecture,
and complete a final project.
- Homework: 30%
- Lectures and participation: 30%
- Final project: 40%
gildea @ cs rochester edu
December 8, 2014
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