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Merge branch 'master' of https://github.com/CompPhysics/MachineLearning

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@@ -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
@@ -62,11 +61,11 @@ 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
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.
@@ -111,6 +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