Machine Learning w. Neural Nets
Instructor: Dr. Mike Bowles
Neural nets were officially pronounced dead in the 90's, but Canadian scholars didn't get the memo. Their thoughtful, persistence has lead to several new discoveries that have resurrected neural nets and made them the best choice for a number of extremely difficult problems like speech recognition and handwriting recognition. This course will cover background on neural nets, their origins, traditional architectures and recent developments such as auto-encoders and restricted Boltzman machines.
Class will meet for 8 sessions on Wed and Thurs evenings from 7:00 to 9:00 pm.
-Apr 3 and 4
-Apr 17 and 18
-Apr 24 and 25
Some of the Neural Net Topics that we'll cover
-What are NN?
-How do they relate to brain?
-How perceptron learns
-Properties of perceptron
-Perceptron for reduced rank approximation
-Batch size effects
-Other methods for performing minimization
Recurrent neural nets
How to regularize neural nets
Deep belief nets
The class will undergraduate level calculus and linear algebra and moderate programming skills. We'll use R and Python primarily. Some familiarity with machine learning problems (e.g. linear regression) will be helpful, but not strictly required.
Registration for the Class
Early Registration - $325
by credit card only - http://machinelearningneuralnets.eventbrite.com
Regular Registration - $375
by credit credit card - http://machinelearningneuralnets.eventbrite.com
by check or cash - in class