This lesson gives an in-depth look into various types of neuronal networks, as well properties, parameters, and phenomena which characterize them. 

In this lesson, you will learn about spiking neuron networks and linear response models.

This lesson discusses Bayesian neuron models and parameter estimation.

This lesson gives an overview of Bayesian memory and learning, as well as how to go from observations to latent variables.

In this lesson, you will learn about how constraints can help us understand how the brain works.

This lesson discusses how to approach neural systems from an evolutionary perspective.

This talk introduces Bayes' theorem, which describes the probability of an event, based on prior knowledge of conditions that might be related to the event.

This lesson recaps why math, in a number of ways, is extremely useful in data science.

This lesson provides an introduction to the lessons in this course that deal with statistics and why they are useful for data science.

In this lesson, users will learn about the importance of exploratory analysis, as well as how statistics enables one to become familiar with and understand one's data. 

This lesson goes over graphical data exploration, including motivations for its use as well as practical examples of visualizing data distributions. 

In this lesson, users learn about exploratory statistics, and are introduced to various methods for numerical data exploration.

This lesson overview some simple descriptions of statistical data.

This lesson covers the basics of hypothesis testing.

This lesson provides instruction on how to infer results from incomplete data.

This lesson provides instruction on finding parameter values, computing confidence levels, and other various statistical methods employed in data investigation.

In this lesson, statistical methods and tools are described for estimating parameters in your dataset.

This lesson covers how to measure the correspondece between data and model.

In this lesson, you will learn the concepts behind choosing useful variables, as well as various analyses and tools to do so.

This lesson goes over some of the common problems in statistical modeling.