Neural Machine Learning I

COMP / ELEC / STAT 502, Spring 2026

This web site may be updated at any time for implementation of emergency measures (such as Covid-19) as necessary, and corresponding changes may occur relative to the posted Syllabus and other posting at this site. 
For those who intend to enroll to this course: If you are an undergraduate you will need my permission to register. If you are a graduate student you do not need my permission 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 help assess your situation.

Class meets: TR 1:00pm - 2:15pm, room TBA (tentatively MXF 252)
This class is based on in-person instruction, subject to changes if emergency measures should require. Classes will not be recorded unless the University mandates remote instruction.
Instructor: Erzsébet Merényi
email: erzsebet@rice.edu
Office/Phone: MXF 229, 713-348-3595  email preferred
Office hour: by appointment
Preferred office hour window: TBD on 1st week of classes
Make appointments in the preferred window if possible
You may also come by in this window unannounced but only an appointment ensures that I am there. You can ask for time outside the preferred window, and I will accommodate your request at my earliest possibility but cannot promise to find time immediately.

Teaching Assistant, grader and contact information:
TBA, email:
Advising by TA(s): by appointment (contact TAs by email)
Sample Course Outline
Syllabus
Required Background
Course Flyer
Disability Allowances
Title IX




Last Updated: December 4, 2025
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. Hands-on coding, simulation and problem-solving practice through homework assignments, and appliaction to research in a 1-month course project (instead of final exam). See details in the Syllabus and under the links below.