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 #2: History of Machine Learning (September 5)
Week #12: Feature Extraction and Image Processing for Object Detection (November 14)