Model Fitting 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 focuses on the purpose of model fitting, approaches to model fitting, model fitting for linear models, and how to assess the quality and compare model fits. We will present a 10-step practical guide on how to succeed in modeling.
- Purpose of model fitting and the structure of linear models
- Linear regression with mean-squared error (MSE)
- Linear regression with maximum likelihood error (MLE)
- Model fitting for linear models
- Multiple linear regression and polynomial regression
- Confidence intervals and bootstrapping
- Bias-variance trade-off
- How to assess the quality of model fits
- How to compare different fitted models
Experience with Python Programming Language