Upon completion of this course, you will be able to:

• Distinguish the concepts of "Artificial Intelligence", "Machine Learning", "Neural Networks", "Deep Learning"
• Distinguish between different types of learning (e.g. supervised, unsupervised, reinforcement) and recognise which applies to their own problem
• Distinguish between linear and non-linear approaches and recognise which is best suited for application to their own problem
• Describe what a feed-forward neural network (FFNN) is, along with its components (neurons, layers, weights, bias, activation functions, cost functions)
• Explain how training of a FFNN works from a mathematical point of view (gradient descent, learning rate, backpropagation)
• Execute with pen and paper a few steps of training of a very simple FFNN model
• Tell the difference between a shallow and a deep network
• Explain broadly how different NN architectures are wired and how they work
• Implement and apply the most appropriate architecture to a given problem/dataset
• Analyze training curves and prediction outputs to evaluate if the training has been successful
• Debug possible issues with the training and suggest changes to fix them
• Explain the difference between training, validation and testing
• Define what overfitting is from a mathematical point of view, and what issues it causes
• Identify what constitutes good practices of dataset design and how to avoid introducing information leakage or other biases when building their own datasets