Model Fitting II (Outro 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 summarizes the concepts introduced in Model Fitting I and adds two additional concepts: 1) MLE is a frequentist way of looking at the data and the model, with its own limitations. 2) Side-by-side comparisons of bootstrapping and cross-validation.
- MLE is a frequentist way of looking at the data and the model, with its own limitations
- Side-by-side comparisons of bootstrapping and cross-validation
- A research example for model fitting. Point out that two main use of the model: 1) parameter estimation and its subsequent interpretation and 2) model simulation: both for the current data set as well as for new predictions beyond the fitted dataset
- General steps for model building in real research
- Why linear models have convex error functions
- Introduction to GLM
Experience with Python Programming Language