Covers handling network data, types of graphs, centrality measures, and properties of real-world networks.
Explores maximum likelihood inference, comparing models based on likelihood ratios and demonstrating with a coin example.
Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
Explores handling network data, including types of graphs, real-world network properties, and node importance measurement.
Covers the basics of networks, focusing on brain networks, historical breakthroughs, small-world and scale-free network discoveries, and the importance of the human connectome.
Covers the properties of the exponential family and the estimation of parameters.
