This seminar oriented course overviews the basic architecture of Peer to Peer (P2P) networks and describes models and tools used for the design and evaluation of such networks. Several examples of P2P networks will be presented, drawing both from well established Internet implementations and from emerging wireless, mobile environments such as vehicular networks. The course will include instructor, student and guest presentations, class quizzes and a term project.

Prerequisites: Graduate standing (open also to a limited number of undergraduate students)

Grade Basis: Grade based on class quizzes, class presentations and project term paper.

This course will investigate the important role that concepts from Artificial Life, an emerging discipline that combines the computational and biological sciences, can play in the construction of advanced computer graphics and vision models for virtual reality, animation, interactive games, active vision, visual sensor networks, medical image analysis, etc. The focus will be on comprehensive models that can realistically emulate a variety of living things-plants and animals-from lower animals to humans. Typically situated in virtual worlds governed by physical laws, such models will often make use of physics-based simulation techniques. More significantly, however, they must also simulate natural processes that uniquely characterize living systems, such as birth and death, growth, natural selection, evolution, perception, locomotion, manipulation, adaptive behavior, learning, and other aspects of higher intelligence. Students will be exposed to the effective computational modeling of these natural phenomena of life and their incorporation into sophisticated, self-animating graphical entities. Specific topics will include modeling plants using L-systems, biomechanical simulation and control of animal and human bodies, behavioral animation, reinforcement and neural-network learning of locomotion, cognitive modeling, artificial animals and humans, human facial animation, artificial evolution, among others.

This graduate seminar course will provide a survey of current research in the area of the genetics of gene expression. Recent developments in high throughput technology enable the measurement of both gene expression levels and genetic variation on a genome wide scale. For this first time, this data allows us to explore questions about how genetic variation affects how genes are regulated. The course will focus on analysis methods of this type of data. The course will both provide the computational and statistical background to understand recent research in this area as well as dive deep into the details of current analysis methods. The course topics will include a background in quantitative trait mapping, variance component methods, co-expression network analysis, Bayesian and causal networks, expression heterogeneity and inter-sample correlation, and integration of other types of data such as protein-protein interaction data. Students will be required to read relevant research papers each week. In addition to short written responses to assigned papers, students will be graded on one presentation of a topic or paper given to the class and on a final project.

See webpage at http://www.cs.ucla.edu/~eeskin/courses/cs229-w08.html.