Covers the fundamental principles and methods of machine learning, including supervised and unsupervised learning techniques.
Covers linear models, logistic regression, decision boundaries, k-NN, and practical applications in authorship attribution and image data analysis.
Introduces key machine learning concepts, such as supervised learning, regression vs. classification, and the K-Nearest Neighbors algorithm.
Covers the k-Nearest-Neighbor classifier, hand-written digit recognition, multi-class k-NN, data reduction, applications, graph construction, limitations, and the curse of dimensionality.
Covers the concept of multiclass classification and the challenges of linearly separating data with multiple classes.
Introduces the basics of machine learning, covering supervised classification, logistic regression, and maximizing the margin.
