Introduction of the Foundations of Machine Learning in Python course - Day 01.
High-Performance Computing and Analytics Lab, University of Bonn
Optimization for machine learning - Day 02 lecture of the Foundations of Machine Learning in Python course.
High-Performance Computing and Analytics Lab, University of Bonn
Linear Algebra for Machine Learning - Day 03 lecture of the Foundations of Machine Learning in Python course.
High-Performance Computing and Analytics Lab, University of Bonn
Support Vector Machines - Day 06 lecture of the Foundations of Machine Learning in Python course.
High-Performance Computing and Analytics Lab, University of Bonn
Decision Trees and Random Forests - Day 07 lecture of the Foundations of Machine Learning in Python course.
High-Performance Computing and Analytics Lab, University of Bonn
Clustering and Density Estimation - Day 08 lecture of the Foundations of Machine Learning in Python course.
High-Performance Computing and Analytics Lab, University of Bonn
Dimensionality Reduction - Day 09 lecture of the Foundations of Machine Learning in Python course.
High-Performance Computing and Analytics Lab, University of Bonn
Introduction to Neural Networks - Day 10 lecture of the Foundations of Machine Learning in Python course.
High-Performance Computing and Analytics Lab, University of Bonn
Introduction to Convolutional Neural Networks - Day 11 lecture of the Foundations of Machine Learning in Python course.
High-Performance Computing and Analytics Lab, University of Bonn
Initialization, Optimization, and Regularization - Day 12 lecture of the Foundations of Machine Learning in Python course.
High-Performance Computing and Analytics Lab, University of Bonn
U-Nets for medical Image-Segmentation - Day 13 lecture of the Foundations of Machine Learning in Python course.
High-Performance Computing and Analytics Lab, University of Bonn
Sequence Processing - Day 15 lecture of the Foundations of Machine Learning in Python course.
High-Performance Computing and Analytics Lab, University of Bonn
This lesson contains practical exercises which accompanies the first few lessons of the Neuroscience for Machine Learners (Neuro4ML) course.
This video briefly goes over the exercises accompanying Week 6 of the Neuroscience for Machine Learners (Neuro4ML) course, Understanding Neural Networks.
This lesson breaks down the principles of Bayesian inference and how it relates to cognitive processes and functions like learning and perception. It is then explained how cognitive models can be built using Bayesian statistics in order to investigate how our brains interface with their environment.
This lesson corresponds to slides 1-64 in the PDF below.
This is a tutorial on designing a Bayesian inference model to map belief trajectories, with emphasis on gaining familiarity with Hierarchical Gaussian Filters (HGFs).
This lesson corresponds to slides 65-90 of the PDF below.
This lecture covers a lot of post-war developments in the science of the mind, focusing first on the cognitive revolution, and concluding with living machines.
This lecture provides an overview of depression (epidemiology and course of the disorder), clinical presentation, somatic co-morbidity, and treatment options.
This lesson is a general overview of overarching concepts in neuroinformatics research, with a particular focus on clinical approaches to defining, measuring, studying, diagnosing, and treating various brain disorders. Also described are the complex, multi-level nature of brain disorders and the data associated with them, from genes and individual cells up to cortical microcircuits and whole-brain network dynamics. Given the heterogeneity of brain disorders and their underlying mechanisms, this lesson lays out a case for multiscale neuroscience data integration.
This tutorial demonstrates how to perform cell-type deconvolution in order to estimate how proportions of cell-types in the brain change in response to various conditions. While these techniques may be useful in addressing a wide range of scientific questions, this tutorial will focus on the cellular changes associated with major depression (MDD).