MACHINE LEARNING WITH NEURAL NETWORKS
Code 1141I
Credits 4
Learning outcomes
Course syllabus:
(1) Introduction to Machine Learning (fundamental problem and its inherent complexity; general approaches for its solution)
(2) Classic Neural Networks models (Hopfield model; recurrent Boltzmann Machines (BM) and Restricted Boltzmann Machines (RBM); learning with BM y RBM: gradient descent, Contrastive Divergence and its variants; single-layer perceptrons (SLP): lineal and logistic regression, Rosenblat perceptron; multi-layer perceptrons (MLP): learning with MLP, back-propagation; Convolutional Neural Networks (CNN): model, link to MLP, and learning)
(3) Deep Learning: link with classical models and modern learning techniques.
(1) Introduction to Machine Learning (fundamental problem and its inherent complexity; general approaches for its solution)
(2) Classic Neural Networks models (Hopfield model; recurrent Boltzmann Machines (BM) and Restricted Boltzmann Machines (RBM); learning with BM y RBM: gradient descent, Contrastive Divergence and its variants; single-layer perceptrons (SLP): lineal and logistic regression, Rosenblat perceptron; multi-layer perceptrons (MLP): learning with MLP, back-propagation; Convolutional Neural Networks (CNN): model, link to MLP, and learning)
(3) Deep Learning: link with classical models and modern learning techniques.