Machine Learning I: Intro Lecture

This lecture is part of the Neuromatch Academy (NMA), a massive, interactive online summer school held in 2020 that provided participants with experiences spanning from hands-on modeling experience to meta-science interpretation skills across just about everything that could reasonably be included in the label "computational neuroscience".

This lecture provides an overview of generalized linear models (GLM) and contains links to 2 tutorials, lecture/tutorial slides, suggested reading list, and 3 recorded question and answer sessions.

Topics covered in this lesson:

• Overview of generalized linear models for different types of output and different likelihoods
• Classifiers and regularizers
• Review of maximum likelihood estimation for the parameters and issues around overfitting and regularization

External Links

• Link to Lecture Slides
• Suggested Reading
• Link to Neuromatch Academy
• Link to discussion forum on Neurostars.org

Prerequisites

• Experience with Python Programming Language.