This course intends to provide a general understanding of some of the main topics in the machine learning field. The subjects that will be discussed during the course are listed below:

I. Supervised learning (2h)
• Decision theory (risk minimization, Bayes-optimal prediction)
• Statistical learning (capacity, under/overfitting, bias/variance, regularization)
• Some popular models

o kNN: k-nearest neighbours
o CART: classification and regression trees
o RF: random forests
o SVM: support vector machines
o ANN: artificial neural networks

II. Unsupervised learning (30min)
• Some popular problems

o structure learning
o outlier detection
o Clustering
o manifold learning
o Sampling

III. Deep learning (1h30)
• Perceptron era: 1958-60s (biological inspiration, McCuloch-Pitts artificial neuron)
• Backpropagation era: 1986-90s (gradient descent, CNNs, RNNs)
• Deep learning era: 2006- (modern activations, resnets, GANs)

Program:

1pm - 3pm: Supervised learning
3pm - 3:15pm: coffee break
3:13 - 5:15 pm: Unsupervised learning + Deep learning

Registration: https://goo.gl/forms/xa4FT7TZ4rq6AE5J3