From e3e369b98fcdf88d5c603ea36734dd2fd79b67c2 Mon Sep 17 00:00:00 2001 From: Morten Hjorth-Jensen Date: Sat, 27 Jul 2019 23:09:19 +0200 Subject: [PATCH 1/3] Update README.md --- README.md | 1 - 1 file changed, 1 deletion(-) diff --git a/README.md b/README.md index 63e1a188a..037d31725 100644 --- a/README.md +++ b/README.md @@ -19,7 +19,6 @@ This course aims thus at discussing many of the central algorithms used in Data - Learn about basic data analysis, statistical analysis, Bayesian statistics, Monte Carlo sampling, data optimization and machine learning; - 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; -- Gain knowledge of central aspects of Monte Carlo methods, Markov chains, Gibbs samplers and their possible applications; - 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 and random forests From 708fd9ffa2e2b336545421f83275015fafe803a4 Mon Sep 17 00:00:00 2001 From: Morten Hjorth-Jensen Date: Sun, 28 Jul 2019 21:40:44 +0200 Subject: [PATCH 2/3] Update README.md --- README.md | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 037d31725..9ee8b9479 100644 --- a/README.md +++ b/README.md @@ -65,7 +65,7 @@ All the above topics will be supported by examples, hands-on exercises and proje Computational aspects play a central role and you are expected to work on numerical examples and projects which illustrate -the theory and methods. We recommend strongly to form small projects of 2-3 participants. Some of the projects can be coordinated with the high-performance programming course IN4200. +the theory and methods. We recommend strongly to form small projects of 2-3 participants. @@ -110,6 +110,4 @@ The link here https://www.mn.uio.no/english/research/about/centre-focus/innovati - _IN5400/INF5860 Machine Learning for Image Analysis_ https://www.uio.no/studier/emner/matnat/ifi/IN5400/. An introduction to deep learning with particular emphasis on applications within Image analysis, but useful for other application areas too. - _TEK5040 Deep learning for autonomous systems_ https://www.uio.no/studier/emner/matnat/its/TEK5040/. The course addresses advanced algorithms and architectures for deep learning with neural networks. The course provides an introduction to how deep-learning techniques can be used in the construction of key parts of advanced autonomous systems that exist in physical environments and cyber environments. - _STK4051 Computational Statistics_ https://www.uio.no/studier/emner/matnat/math/STK4051/index-eng.html -- _STK4021 Applied Bayesian Analysis and Numerical Methods_ https://www.uio.no/studier/emner/matnat/math/STK4021/index-eng.html - - +- _STK4021 Applied Bayesian Analysis and Numerical Methods_ https://www.uio.no/studier/emner/matnat/math/STK4021/i From 510a25ebed8f3019cec2acdd98f9790da51d8e85 Mon Sep 17 00:00:00 2001 From: Morten Hjorth-Jensen Date: Mon, 29 Jul 2019 09:42:58 +0200 Subject: [PATCH 3/3] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 9ee8b9479..40daa0714 100644 --- a/README.md +++ b/README.md @@ -61,7 +61,7 @@ The following topics will be covered - Boltzmann Machines - Dimensionality reduction, from PCA to cluster models -All the above topics will be supported by examples, hands-on exercises and project work. +Hands-one demonstrations, exercises and projects aim at deepining your understanding of these topics. Computational aspects play a central role and you are expected to work on numerical examples and projects which illustrate