|
HU Credits:
2
Degree/Cycle:
1st degree (Bachelor)
Responsible Department:
Computer Sciences
Semester:
2nd Semester
Teaching Languages:
Hebrew
Campus:
E. Safra
Course/Module Coordinator:
Dr. Tamar Friedlander
Coordinator Office Hours:
Thursdays 15:00-16:00
Teaching Staff:
Dr. Tamar Friedlander,
Dr. Oren Forkosh
Course/Module description:
The course will survey various cases of optimization and computation in natural high-dimensional biological systems.
Course/Module aims:
Learn about optimization in natural biological systems.
Learning outcomes - On successful completion of this module, students should be able to:
The students will familiarize themselves with various examples for optimization and computation in natural biological systems, such as evolution in biological populations.
The students will apply the theory and be able to simulate evolution in a natural population.
Attendance requirements(%):
Attendance in the project presentation session is required.
Teaching arrangement and method of instruction:
Lectures, home assignments
Course/Module Content:
1. What is the difference between natural and artificial systems and how they approach optimization problems?
1. 2. Stochastic vs. deterministic optimization: the role of population size.
2. 3. How does evolution solve optimization problems? Fitness and natural selection.
3. 4. Fitness landscapes: how evolution navigates in very-high dimensional space.
4. 5. Genetic algorithms.
5. 6. The role of population diversity in stochastic optimization.
7. Complex systems. How simple and local rules of interaction allow animals to solve complicated
problems such as foraging for food, finding mates, or evading predators? Tools: Reynold’s Boids
(artificial life, computer graphics), Game of life, Nonlinear dynamics and Chaos, Graph theory
8. Constrained optimizations. Ways in which evolution effects animal behavior or what can we
learn from economics about natural selection: from rock-paper-scissors games to pareto
optimality. Tools: Stochastic processes, Game theory
9.. Being different. Personality. The advantages (and disadvantages) of being different, about group
synergy, cooperation, and competition. Tools: Dimensionality reduction
10. Animal communication. How to transfer information and make sure it reaches the ‘right’ ears?
Tools: Bayesian networks
11. Social interactions. Top-down approaches to looking at behavior: from the group to the
individual. Tools: Information theory, Maximum Entropy models
12. Methods. Using machine-learning tools to understand animals: from virtual reality
environments for animals to tracking the movement of the mouse’s tail. Tools: Deep learning
13. Emotions. What are emotions? Why do we have them? and when is it bad to feel? Tools: Dynamical systems
Required Reading:
none
Additional Reading Material:
“Collective Animal Behavior” by David J. T. Sumpter
Course/Module evaluation:
End of year written/oral examination 0 %
Presentation 10 %
Participation in Tutorials 0 %
Project work 50 %
Assignments 40 %
Reports 0 %
Research project 0 %
Quizzes 0 %
Other 0 %
Additional information:
|
