====== CMPU-395: Machine Learning ======


===== Aim and Scope =====
The brain is arguably the most capable information processing device
known and has been the source of inspiration for a number of
algorithms.  In this course we review models of brain function, from
numerical simulations of activity in real neurons and small neural
networks, via abstract artificial neural networks to machine learning
algorithms for classification and prediction based on experience
rather than predefined rules.  Topics covered include Hodgkin-Huxley
and integrate-and-fire models, central pattern generators, attractor
models of associative memory, layered networks and error driven
learning, competitive learning and formation of topology preserving
maps, and behavior formation via reinforcement learning.




===== Syllabus =====


==== Instructor ====
[[/people/ekeberg/top|Örjan Ekeberg]] is a STINT Fellow at Vassar during the fall 2009.
He is an associate professor in Computer Science at the Royal Institute of Technology
in Stockholm, Sweden, where he also works for the Stockholm Brain Institute.

  * **Office**: OLB 106
  * **Office hours**: Mon 15:30--16:30, Thu 11:00--12:00 or by appointment
  * **E_mail**: ekeberg@cs.vassar.edu

==== Textbook ====
[[http://www.crcpress.com/product/isbn/9781420067187|Stephen Marsland: Machine Learning, an Algorithmic Perspective;
CRC Press, 2009]]

==== Schedule ====

Lectures are on Mondays and Wednesdays, 1:30 to 2:45, in OH 201.

  * [[schedule|Plan for the lectures.]]

//Final exam will be Friday, December 11, between 6pm and 8pm, room OLB 105//

==== Assignments ====

Six assignments will be given during the course.

  * [[assignments|Instructions for the assignments.]]


==== Requirements and Grading ====

Participation during lectures and completion of six bi-weekly assignments are required.

Grading will be weighted like this:
| 60% | Assignments           |
| 10% | Lecture participation |
| 30% | Final exam            |




==== Students with disabilities ====

Academic accommodations are available for students with disabilities who are registered with 
the Office of Disability and Support Services. Students in need of disability accommodations 
should schedule an appointment with me early in the semester to discuss any accommodations for 
this course which have been approved by the Office of Disability and Support Services, as 
indicated in your DSS accommodation letter.

==== Students with abilities ====

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