About the course

Attendance to the lectures and laboratories of this module is compulsory.

Prerequisites

In addition to excellent programming skills, Matrix Algebra and very good knowledge of Calculus (derivatives) is required for this course. Python or Matlab will be used for the laboratory exercises.

Evaluation

The course is evaluated by a formal exam (65%) and lab-based assignments/reports (35%), i.e. 15% report on unsupervised learning, 20% report on reinforcement learning.

Bibliography

The reading material for this course includes a selection from the books:

• “Introduction to the Theory of Neural Computation” by Hertz, Krogh & Palmer
• “Spiking Neuron Models” by Gerstner & Kistler
• “Theoretical Neuroscience” by Dayan & Abbot and “Reinforcement Learning: An Introduction” by Sutton & Barto.

About the course materials

Yet to come

Working with the notebooks

1. Python: Using Microsoft Azure
2. MATLAB

Installing Python and MATLAB on your personal computer