F5611 Machine Learning for Astronomers (Fall 2020)
Martin Topinka & Matej Kosiba
https://is.muni.cz/predmet/sci/podzim2020/F5611
2h every 2 weeks, on Tuesday 5-7 PM Environment: Zoom
- Introduction of ourselves
- Introduction of the course
- Bit of history and motivation for ML
- Classification vs Regression
- Supervised vs Unsupervised
- Principles of learning
- Light-speed overview of Python, GitHub, Jupyter notebook
Introduction to machine learning, history... Principles of machine learning Classification vs regression Supervised, unsupervised machine learning Loss function, accuracy measures Bias-variance tradeoff Curse of dimensionality Python based software for machine learning Basic machine learning algorithms (SVM, KNN, K-mean, Logistic regression, Decision Trees, Random Forest) Feature selection, data reduction (PCA) Advanced algorithms (bagging, boosting, voting) Introduction to scikit-learn First touch of scikit-learn API scikit-learn practical session (with GRB classification, QSO’s vs stars…) Model validation, hyper-parameter fine tuning Imbalanced classes Neural network, perceptron Deep learning neural networks Regularisation, dropout Deep learning with Convolutional Neural Networks Encoder-Decoder, Auto-encoder GAN Training data generators Introduction to Keras/TensorFlow Hands on session in Keras (developing a NN to classify stars/QSOs; developing a deep convNN auto-encoder for finding transients) Optional: Gaussian Processes