Bulletin description: Introduction to topics in machine learning through an applied perspective. The course assumes basic fluency in programming and mathematics at the single-variable calculus level, and will include learning specific machine learning concepts (listed below), their historical origins, and existing and potential applications to modern society.

Machine learning concepts studied will include: classification (including naive Bayes, support vector machines, kernel methods, and neural networks), regression (including spline interpolation and linear and polynomial regression), mixture of Gaussians clustering, object detection (including convolutional neural networks, feature extraction, edge detection, and processing methods), principal component analysis, and evaluation of machine learning models.

Week #1: Introduction to Machine Learning and Python (August 29) [notebook]

Week #2: History of Machine Learning (September 5)

Week #3: Splines and Linear and Polynomial Regression (September 12) [notebook]

Week #4: Evaluation of Machine Learning Models (September 19) [notebook]

Week #6: Naive Bayes Classifiers (October 3) [notebook]

Week #7: Support Vector Machines and Kernel Methods (October 10) [notebook]

Week #8: Principal Component Analysis (October 17) [notebook]

Weeks #10: Neural Networks (October 31) [notebook]

Weeks #11: Convolutional Neural Networks (November 7) [notebook]

Week #12: Feature Extraction and Image Processing for Object Detection (November 14)

Week #14: Mixture of Gaussians Clustering (December 5) [notebook]