First Course in Machine Learning

Linear Modelling: A Least Squares Approach. Linear Modelling: A Maximum Likelihood Approach. The Bayesian Approach to Machine Learning. Bayesian Inference. Classification. Clustering. Principal Components Analysis and Latent Variable Models. Further Topics in Markov Chain Monte Carlo. Classification and Regression with Gaussian Processes. Dirichlet Process models.

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• Aprendizagem mecânica
• Econometria
• Mineração de Dados
• Teoria matemática da computação
• Engenharia do controlo automático
• Probabilidade e Estatística
• Economia estatística
• Bases de Dados

MATLAB Script;Posterior Density;Machine Learning;Gibbs Sampling;machine learning algorithms;Laplace Approximation;model specifications;Marginal Likelihood;parameter values;Mixture Models;least squares;GP Prior;Linear modeling;Decision Boundaries;Maximum Likelihood;Coin Toss;Bayesian Inference;Latent Dirichlet Allocation;classifiers;Coin Landing Heads;clustering;Random Variables;Principal components analysis;Training Points;Latent variable models;Covariance Matrix;Posterior Probability;Multivariate Gaussian;Map Solution;Infinite Mixture Model;HDPs;Proposal Density;Conditional Distributions;True Posterior;Approximate Posterior;Class Conditional Distributions;Standard Mixture Model