Fall 2007
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COM SCI 239 Current Topics in Programming Languages and Systems | |||||||
LEC 1 PALSBERG, J. | |||||||
ID Number | Type | Sec | Days | Start | Stop | Bldg | Rm |
587232201 | LEC | 1 | MW | 2:00P | 3:50P | BOELTER | 5272 |
Title:
Parallel Programming Languages
Motivation for the course: learn new programming abstractions that will be useful for programming multi-core computers. The course will be almost entirely about papers on parallel programming languages, most of them less than two years old. The professor will give half of the lectures, and the participants will give lectures too! Recommended Prerequisites: One of CS 231: Types and Programming Languages CS 232: Static Program Analysis CS 234: Computer-Aided Verification If you want to take the course and lack the prerequisite, please contact Prof. Jens Palsberg (palsberg AT cs DOT ucla DOT edu). | |||||||
COM SCI 259 Current Topics in System Design and Architecture | |||||||
LEC 1 POTKONJAK, M. | |||||||
ID Number | Type | Sec | Days | Start | Stop | Bldg | Rm |
587357201 | LEC | 1 | MW | 2:00P | 3:50P | BOELTER | 5273 |
Title:
Modeling and Optimization Techniques for Computer Science and Engineering
The course covers advanced topics in applied large scale optimization and statistical modeling techniques ranging from various types of heuristics and iterative improvement techniques to simulated annealing and large neighborhood search to integer linear programming (ILP) to nonlinear programming (NLP) to search techniques and to basic non-parametric statistics (e.g. smoothing, non-linear regression, maximum likelihood). The emphasis is on intuition, practical issues and in particular on applications in computer science, computer engineering, electrical engineering and emerging new scientific fields. The class will have three large homeworks that aim to enable students learn how to use ILP and NLP and how to use basic modeling software (R) and techniques. We will use data sets and traces from deployed systems. Recommended Prerequisites: There are no formal prerequisites. Grade Basis: The homeworks will be assigned to small teams (2 students). The grading will be done using scores on homeworks (75%) and class participation and presentations (25%). | |||||||