A key part of data science -- and the part most people immediately gravitate towards -- is the use of machine learning techniques to build descriptive or predictive models of data. This module covers the basics of unsupervised and supervised machine learning.
For those students new to machine learning and coming from other disciplines, an introductory lecture and additional introductory material on classification, assosiation rules, clustering, and linear regression will help them better understand the content of the other lectures.
- Matrices, features, and unsupervised machine learning:
- Unsupervised machine learning slides (basic).
- Unsupervised machine learning companion Jupyter notebook (basic).
- Overview of supervised machine learning:
- Overview, decision trees, and random forests slides (basic).
- Overview, decision trees, and random forests video (basic).
- Overview, decision trees, and random forests video (basic).
- Overview, decision trees, and random forests video (basic).
- Overview, decision trees, and random forests video (basic).
- Overview, decision trees, and random forests video (basic).
- Overview, decision trees, and random forests companion Jupyter notebook (basic).
- Logistic and linear regression:
- Logistic and linear regression slides (basic).
- Logistic and linear regression video (basic).
- Logistic and linear regression companion Jupyter notebook (basic).
- Logistic and linear regression companion video (basic).
- Neural networks:
- Basic neural networks and gradient descent slides (intermediate).
- Basic neural networks and gradient descent companion Jupyter notebook (intermediate).
- OVERVIEW-MACHINE-LEARNING-Models-intro slides
- ASSOCIATION-RULES-Mining-Apriori-intro slides
- CLUSTERING
- DATA-CLASSIFICATION-Decision-Tree-KNN-intro slides
- REGRESSION-Linear-Residuals-Metrics-intro slides
- REGRESSION-Linear-Residuals-Metrics-intro video
- Machine learning Homework
- Transfer learning:
- TRANSFER-LEARNING-Computer-Vision-intermediate.pptx slides (intermediate)
- TRANSFER-LEARNING-Computer-Vision-Tensorflow-intermediate.ipynb Jupyter Notebook (intermediate)
- Initial release, Susan Davidson and Zachary Ives, University of Pennsylvania, February 2020.
- Supporting introductory material, Xumin Liu, Rochester Institute of Technology, August 2022.
- Module on transfer learning, Sumedh Datar, November 2022.
- Videos on overview of supervised machine learning with decision trees and random forests, Tomislav Galjanic, Columbia University, November 2022.
- Videos on K-means clustering, Varalika Mahajan, Columbia University, February 2023.
- Video on linear regression, residuals and metrics, Lylybell Teran, Columbia University, March 2023.
- Video on linear and logistic regression, Lylybell Teran, Columbia University, March 2023.
- Videos on linear and logistic regression Jupyter notebook, Lylybell Teran, Columbia University, March 2023.