Neural Machine Learning I

COMP / ELEC / STAT 502, spring 2023

This web site may be updated at any time for implementation of Covid-19 measures as necessary, changes may occur relative to the posted Syllabus and other posting at this site. 
If you intend to enroll to this course: If you are an undergraduate you will need my permission. If you are a graduate student you do not need my permission to register, but you may contact me if you are unsure about your preparedness. In both cases, please check the Required Background to assess your eligibility/preparedness and then send me the information I list there so that I can assess your situation.

Class meets: TR 2:30 - 3:45pm, room DCH 1064
In-person instruction planned, subject to changes due to Covid-19 measures
Instructor: Erzsébet Merényi
email: erzsebet@rice.edu
Office/Phone: MXF 229, 713-348-3595  email preferred
Office hour: by appointment
Preferred window: Tuesday 4:00 - 5:30pm
Make appointments in this window if possible.
You may also come by unannounced, in-person or in Zoom, but only an appointment ensures that I am there. Zoom links were sent out in Canvas Announcement. You can ask for time outside the preferred window, I just cannot promise to find time immediately.

Teaching Assistant and contact information:
Luis Sanchez las19@rice.edu
Advising by TA: by appointment las19@rice.edu
Preferred window is Monday 4:00 - 5:30pm



Sample Course Outline
Syllabus
Required Background
Course Flyer
Disability Allowances





Last Updated: March 20, 2023
Welcome to biologically inspired neural information processing!
Short course description: Review of major Artificial Neural Network paradigms. Analytical discussion of supervised and unsupervised learning. Emphasis on state-of-the-art Hebbian (biologically most plausible) learning paradigms and their relation to information theoretical methods. Applications to data analysis such as pattern recognition, clustering (information discovery), classification, non-linear PCA, independent component analysis, with examples from image and signal processing and other areas. 

2022 Pizza Points