CS
678 - Advanced Neural Networks and Machine Learning
Syllabus
Fall 2009, TuTh 1:35-2:50pm, 3718
HBLL
Professor: Tony Martinez, 3326 TMCB, x6464, http://axon.cs.byu.edu/~martinez
Office Hours: by appointment
Website: http://axon.cs.byu.edu/~martinez/classes/678/
Goals: Continuation in the study of the philosophy, utility, and
models of machine learning, such that students are able to propose original
research with potential follow-up in a graduate research program. Expand the creativity of the students
in all aspects of computing.
Prerequisites: CS 478 (Introduction to Neural Networks
and Machine Learning), Creativity.
Text: Papers available from the class website. Also, we may use some chapters from Machine Learning
by Tom Mitchell. You are expected
to read the assigned literature before and optionally after the scheduled lecture.
Grading (~): Assignments: 10%, Midterm: 30%, Project: 30%, Final: 30%.
Grading is on a curve and some amount of subjectivity is allowed for
attendance, participation, perceived effort, etc. If you think, you'll be all right.
The mid-term exam will be in the testing center and final exam will be
in class on Thursday, Dec. 17 from 2:30pm-5:30pm.
Late assignments: Assignments are expected on time
(beginning of class on due date).
Late papers will be marked off at 5%/school day late. However, if you have any unusual
circumstances (sick, out of town, unique from what all the other students have,
etc.), which you inform me of, then I will not take off any late points. Nothing will be accepted after the last
day of class instruction.
Topics (To be
covered in the following order):
Advanced Backpropagation Concepts
(On-line vs. Batch, Higher Order nets, Lazy Learning, Oracle Learning)
Local models (Radial Basis Function,
Distance Metrics)
Support Vector Machines
Hopfield Networks
Boltzmann Machines
HMM (with Baum Welch Learning)–
with Speech as the example platform
MULTCONS, Hopfield Extensions
Rule Based Learning (Sequential
Covering, CN2)
Unsupervised Learning (K-means,
Agglomerative, Competitive, SONs, Conceptual
Clustering)
Recurrent Neural Networks (Elman Nets,
BPTT, RTRL)
Next Group of
Topics as Time Allows (Based on your requests):
Reinforcement Learning
Feature Selection
Computational Learning Theory
Kalman Filters
Manifold Learning/Dimensionality
Reduction
Transfer Learning