Schedule
The course is organized in three-hour sessions in the morning and two-hour sessions in the afternoon. This schedule leaves ample time to assimilate the material, run codes and exchange experience with classmates and the instructor.
Day 1. Introduction to Supervised and Unsupervised Machine Learning
Wednesday, 13 January 2021
- What is Machine Learning
- Supervised vs Unsupervised Learning
- Linear Regression
- Logistic Regression
- K-Means
- Principal Component Analysis.
Day 2. Feed-Forward Neural Networks
Thursday, 14 January 2021
- From Logistic Regression to Single Neuron Representation
- Feed-Forward Neural Networks
- Back-Propagation
- Batch, Stochastic and Mini-Batch Gradient Descent
Day 3. Bias and Variance in Neural Networks
Friday, 15 January 2021
- Overfitting and Underfitting
- Regularization
- Batch Normalization
- Machine-Learning Diagnostics
- Training Set, Validation Set, Test Set and k-Fold Validation
Day 4. Convolutional Neural Networks
Monday, 17 January 2021
- Convolution Layers: Definition and Notations
- Pooling and Fully Connected Layers
- Transfer Learning
- Large Datasets and Well-Known Convolutional Neural Networks
Day 5. Recurrent Neural Networks
Tuesday, 18 January 2021
- Recurrent Networks
- Long Short-Term Memory (LSTMs)
- Moving towards Generative Networks: Probabilities for Machine Learning
- Large Datasets and Well-Known Convolutional Neural Networks
Day 6. Generative Neural Networks
Wednesday, 19 January 2021
- Autoencoders
- Variational Autoencoders
- Generative Adversarial Networks and some of their Applications in Geoscience
Deep Learning
Deep learning is broad family of machine learning methods based on artificial neural networks. Applications are extremely diverse.
The course is full. You can still register on a waiting list:
