Courses

Summary of the courses 2019

• Gravitational Lensing

Contents:

1. History of gravitational lensing. Lensing theory 1: from deflection angles to point-source lens.

2. Galaxies as lenses.

3. Lens modelling and source reconstruction

4. Finding gravitational lenses: from visual inspection to machine learning

5. Lenses as cosmological tools: dark matter substructure.

• Machine Learning and its Applications in Astronomy

Contents:

1. Introduction to machine learning, how many nearest neighbors should I choose for my k-nearest neighbor model?

2. Logistic regression.

3. Optimization and model tuning of neural networks.

4. Introduction to time series and tools for analyzing time series.

5. Introduction to unsupervised learning, kmeans clustering, Gaussian mixture models.