Monte Carlo Methods for Computation and Optimization (Spring 2015)
Syllabus (pdf)
2015 Lecture Notes:
- Lecture 1 : Introduction
- Lecture 2: Random Variable Generation
- Lecture 3: Variance Reduction Methods, I
- Lecture 4: Importance Sampling
- Lecture 5: Sequetial Importance Sampling; Slides for section 5.3: Particle Filters
- Lecture 6: Markov Chain Monte Carlo
- Lecture 7: Some Topics in Brief
Final Assignments
Homework:
- Problem Set 1 Submission April 29.
- Problem Set 2 (parts a+b). Submission June 3
- Problem Set 3 Submission June 24
Homework and Assignment Grades
Slides of Student Presentations:
- Ariel: Simulated Annealing for Constrained Global Optimization
- Ayal: N-grams in MC Tree Search
- Gal: Modern Floor Planning with Simulated Annealing
- Nir: Reversible Jump Markov Chain Monte Carlo
- Niv: MC Simulation of Security Prices
- Oron: Computing Approximate Nash Equilibria
- Noam: Cross Entropy for Monte Carlo Trees Search