Sumários
Linear Models and Multilayer Perceptrons
25 Outubro 2016, 13:00 • João Marques Silva
This week's lecture started with an overview of last week's lecture regarding linear models. Afterwards, the lecture focused on the multilayer perceptron. A number of topics related with the multilayer perceptron were studied. These included the organization of the multilayer perceptron, and the use of the backpropagation algorithm for training multilayer perceptrons.
Introduction to R
18 Outubro 2016, 15:00 • João Marques Silva
This week's practical covered a number of exercises with the R programming language, highlighting generation and plotting of datasets, training the perceptron, and least squares regression..
Itemset Mining and Rule Models
18 Outubro 2016, 13:00 • João Marques Silva
The first part of this week's lecture analyzed an example of itemset mining, including how to find frequent itemsets, and how to then find association rules. The second part of the lecture started the study of linear models. The lecture reviewed the basic linear classifier, and studied least squares regression, the use of the perceptron, and provided an initial motivation for support vector machines.
Introduction to R
11 Outubro 2016, 15:00 • João Marques Silva
This week's practical covered a number of exercises with the R programming language, highlighting important aspects of the language.
Tree models and rule models
11 Outubro 2016, 13:00 • João Marques Silva
The fourth lecture reviewed the construction of decision trees and covered three main topics. First, we introduced notation and definitions to be used throughout the semester. These include definitions for binary classification, formalization of different ML scenarios, and definitions of coverage plots and curves and associated ROC plots and curves. Second, we continued the study of tree models, by relating learned decision trees with coverage curves. Third, we studied rule models, namely the inference of lists of rules and sets of rules. In addition, we studied two examples of descriptive ML: subgroup discovery and association rules. We also studied frequent itemset mining, to be used for learning association rules.