Introduces the fundamentals of regression in machine learning, covering course logistics, key concepts, and the importance of loss functions in model evaluation.
Delves into the intersection of physics and data in machine learning models, covering topics like atomic cluster expansion force fields and unsupervised learning.
Covers the fundamentals of multilayer neural networks and deep learning, including back-propagation and network architectures like LeNet, AlexNet, and VGG-16.
Introduces the Applied Data Analysis course at EPFL, covering a broad range of data analysis topics and emphasizing continuous learning in data science.
Covers Convolutional Neural Networks, including layers, training strategies, standard architectures, tasks like semantic segmentation, and deep learning tricks.
Covers optimization in machine learning, focusing on gradient descent for linear and logistic regression, stochastic gradient descent, and practical considerations.
Covers a review of machine learning concepts, including supervised learning, classification vs regression, linear models, kernel functions, support vector machines, dimensionality reduction, deep generative models, and cross-validation.
