Spiking neuron networks and linear response models.
Bayesian neuron models and parameter estimation.
Bayesian memory and learning, how to go from observations to latent variables.
Constraints can help us understand how the brain works.
Approaching neural systems from an evolutionary perspective
The probability of a hypothesis, given data.
Why math is useful in data science.
Why statistics are useful for data science.
Statistics is exploring data.
Graphical data exploration
Numerical data exploration
Simple description of statistical data.
Basics of hypothesis testing.
Inferring results from incomplete data
Finding parameter values, confidence intervals.
Methods for estimating parameters.
Measuring the correspondece between data and model.
How to choose useful variables.
Common problems in statistical modelling.
Common problems in statistical modelling.