Supervised Machine Learning

This lecture includes a description of supervised ML approaches using tabular data, linear models, tree-based models, ensemble-based models, and preprocessing approaches.

Unsupervised Machine Learning

This lecture includes a description of network-valued data and connectomics and community-detection in network-valued data.

YouTube Lecture | Slides

YouTube Lecture | Slides

Linear Regression

Gradient Descent Concepts

Gradient Descent Coding

Logistic Regression

Decision Trees

Jupyter Notebook from lecture

Network Machine Learning Book 2.2.1: Installation Requirements

Network Machine Learning Book Chapter 3: Network-valued data

Network Machine Learning Book Chapter 4.2: Stochastic Block Models (SBMs)

Network Machine Learning Book Chapter 4.3: Random Dot Product Graphs (RDPGs)

Network Machine Learning Book Chapter 5.1: Estimation in SBMs

AI/ML Week 2 Data Exercise

AI/ML Week 2 Solutions