- Introduction to deep and machine learning
- Classifiers and metrics for evaluation
- Supervised and unsupervised algorithms
- Convolutional neural and recurrent networks
- Methodologies for 1D and 2D signals: sliding windows, super-pixels, streaming data and whole pictures transformation
- Methodologies for conducting deep learning experiments
- Experimental design with mice: quantitative comparison, ARRIVE and 3Rs
- Optional language course (Greek)