Update README.md

2020-07-25 21:22:23 +02:00
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@@ -47,16 +47,15 @@ The following topics will be covered
 - Central elements of Bayesian statistics and modeling;
 - Gradient methods for data optimization
 - 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;
 - Principal Component Analysis and its mathematical foundation

 ### Machine learning

-The following topics are planned for the fall semester 2020
+The following topics will be covered
 - Linear Regression and Logistic Regression;
 - Neural networks and deep learning, including convolutional and recurrent neural networks
- Decisions trees and nearest neighbor algorithms
+- Decisions trees, Random Forests, Bagging and Boosting
 - Support vector machines
 - Bayesian linear and logistic regression
 - Boltzmann Machines