Probabilistic Algorithms

The goal of the course is to introduce the principles, algorithms, and practical aspects of stochastic optimization, based on a number of practical applications drawn from engineering, statistics, and computer science. The course covers a broad range of today’s most widely used probabilistic algorithms, including random search, stochastic approximation, model selection, machine (reinforcement) learning, simulation-based optimization, Markov chain Monte Carlo, optimal experimental design, simulated annealing and recursive linear estimation.
 

Learning Outcomes: 

Outcomes:

  • differentiate the different types of probabilistic algorithms
  • identify problems for which probabilistic algorithms provide an appropriate solution
  • describe the main characteristics of probabilistic algorithms
  • program representative algorithms of each category of algorithms
  • model problems in such a way that they are solvable by probabilistic algorithms
     
Type: 
Course
Semester: 
A2017
ECTS: 
5
Tracks: 
Lecturer: 
Site: 
N
Code: 
42014
Language: 
english
Period: 
weekly
Schedule: 
Monday: 13:15 - 17:00
Location: 
UniNE, Abram-Louis-Breguet 2
Room: 
R.107
Comment: 

First Lecture
The first lecture will take place on Monday, 25.09.2017 at 13:15 in UniNE, Abram-Louis-Breguet 2, room R.107.
 


Organizer
The organizer of this teaching unit and its evaluation is the Faculty of Economics and Business of the University of Neuchâtel. For more details, please visit this web site.