lars/FYS-STK4155
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Update README.md

Browse Source
This commit is contained in:
Morten Hjorth-Jensen
2020-04-18 22:44:13 +02:00
committed by GitHub
parent 100e94f00c
commit a8d46164a1
1 changed files with 4 additions and 4 deletions
Download Patch File Download Diff File Expand all files Collapse all files
README.md
+4 -4
View File
@@ -14,14 +14,14 @@ which is exactly what machine learning aims at providing.
## Learning outcomes
This course aims at giving you insights and knowledge about many of the central algorithms used in Data Analysis and Machine Learning. The course is project based and through various numerical projects, normally three, you will be exposed to fundamental research problems in these fields, with the aim to reproduce state of the art scientific results. Both supervised and unsupervised methods will be covered. You will learn to develop and structure large codes for studying different systems where Machine Learning is applied to, get acquainted with computing facilities and learn to handle large scientific projects. A good scientific and ethical conduct is emphasized throughout the course. More specifically, after this course you will
This course aims at giving you insights and knowledge about many of the central algorithms used in Data Analysis and Machine Learning. The course is project based and through various numerical projects, normally three, you will be exposed to fundamental research problems in these fields, with the aim to reproduce state of the art scientific results. Both supervised and unsupervised methods will be covered. You will learn to develop and structure large codes for studying different cases where Machine Learning is applied to, get acquainted with computing facilities and learn to handle large scientific projects. A good scientific and ethical conduct is emphasized throughout the course. More specifically, after this course you will
- 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;
- 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
- 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
- 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++.
@@ -48,7 +48,7 @@ The following topics will be covered
- Monte Carlo methods, Markov chains, Metropolis-Hastings algorithm;
- Linear methods for regression and classification;
- Estimation of errors using cross-validation, blocking, bootstrapping and jackknife methods;
- Practical optimization using Singular-value decomposition and least squares for parameterizing data.
- Principal Component Analysis and its mathematical foundation
### Machine learning
@@ -61,7 +61,7 @@ The following topics will be covered
- Boltzmann Machines
- Dimensionality reduction, from PCA to cluster models
Hands-one demonstrations, exercises and projects aim at deepining your understanding of these topics.
Hands-on 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