Linear Models: ClassificationExplores linear models for classification, including logistic regression, decision boundaries, and support vector machines.
Decision and regression treesExplores decision and regression trees, impurity measures, learning algorithms, and implementations, including conditional inference trees and tree pruning.
Machine Learning FundamentalsIntroduces the basics of machine learning, covering supervised classification, logistic regression, and maximizing the margin.
Linear Models for ClassificationExplores linear models for classification, logistic regression, decision boundaries, SVM, multi-class classification, and practical applications.
Logistic Regression: Part 1Introduces logistic regression for binary classification and explores multiclass classification using OvA and OvO strategies.
Neural Networks: Multilayer LearningCovers the fundamentals of multilayer neural networks and deep learning, including back-propagation and network architectures like LeNet, AlexNet, and VGG-16.
Bayesian Inference: Part 2Explores Bayesian inference, multiclass classification, logistic regression, and linear regression inference.
Multiclass ClassificationCovers the concept of multiclass classification and the challenges of linearly separating data with multiple classes.