Machine Learning FundamentalsIntroduces the basics of machine learning, covering supervised classification, logistic regression, and maximizing the margin.
Neural Networks: Multilayer PerceptronsCovers Multilayer Perceptrons, artificial neurons, activation functions, matrix notation, flexibility, regularization, regression, and classification tasks.
Nonlinear Supervised LearningExplores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.
Multilayer Networks: First StepsCovers the preparation for deriving the Backprop algorithm in layered networks using multi-layer perceptrons and gradient descent.
Supervised Learning OverviewCovers CNNs, RNNs, SVMs, and supervised learning methods, emphasizing the importance of tuning regularization and making informed decisions in machine learning.
Pytorch Intro: MNIST and DigitsCovers Pytorch basics with MNIST and Digits datasets, focusing on training neural networks for handwritten digit recognition.
Statistical Physics of LearningOffers insights into the statistical physics of learning, exploring the relationship between neural network structure and disordered systems.