From bc24dffd9ba6376ed30aaa80e18c40c59a859dc5 Mon Sep 17 00:00:00 2001 From: Morten Hjorth-Jensen Date: Sat, 4 Jul 2020 23:11:04 +0200 Subject: [PATCH] Update README.md --- README.md | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 88ff62dc1..33586679f 100644 --- a/README.md +++ b/README.md @@ -20,10 +20,11 @@ This course aims at giving you insights and knowledge about many of the central - Be capable of extending the acquired knowledge to other systems and cases; - Have an understanding of central algorithms used in data analysis and machine learning; - Understand linear methods for regression and classification, from ordinary least squares, via Lasso and Ridge to Logistic regression; -- Learn about various neural networks and deep learning methods for supervised and unsupervised learning; -- Learn about about decision trees, random forests, bagging and boosting methods -- Learn about support vector machines and kernel transformations -- Reduction of data sets, from PCA to clustering, supervised and unsupervised methods +- Learn about neural networks and deep learning methods for supervised and unsupervised learning. Emphasis on feed forward neural networks and convolutional neural networks; +- Learn about about decision trees, random forests, bagging and boosting methods; +- Learn about support vector machines and kernel transformations; +- Reduction of data sets, from PCA to clustering; +- Autoencoders and Reinforcement Learning; - Work on numerical projects to illustrate the theory. The projects play a central role and you are expected to know modern programming languages like Python or C++. ## Prerequisites