Predictive Methods of Data Mining

Objectives

The curricular unit of data mining primary goal is to provide students with a fundamental understanding of Predictive Analytics as it relates to improving business performance.

This course will cover the basics of predictive analytics and modelling data to determine which algorithms to use and to understand the similarities and differences and which options affect the models most.

Topics covered include predictive analytics algorithms for supervised learning, including decision trees, neural networks, k-nearest neighbour, support vector machines, and model ensembles.

At the end of the course, participants will be able to use these skills to produce a fully processed data set compatible with building powerful predictive models that can be deployed to increase profitability.

General characterization

Code

200166

Credits

7.5

Responsible teacher

Roberto André Pereira Henriques

Hours

Weekly - Available soon

Total - Available soon

Teaching language

Portuguese. If there are Erasmus students, classes will be taught in English

Prerequisites

None

Bibliography

Teaching method

The course is based on a mix of theoretical lectures and practical lectures and tutorials. The theoretical sessions include the presentation of theoretical concepts and methodologies as well as application examples.

The main objective of the practical classes is to familiarize students with the software to perform the analysis and data explorations tasks.

Evaluation method

Regular examination period (1st examination period)

Exam (60%)
Project (40%)

Resit examination period (2nd examination period)

Exam (60%)
Project (40%)

Minimum grade of 8.0 (in 20) for the exam
Minimum grade of 4.0 (in 20) for the project

Subject matter

The course is divided in the following learning units (LU):

LU1. Introduction to predictive modeling

LU2. Data pre-processing

LU3. Introduction to classifiers

Linear and Logistic regression
Bayesian learning systems
Instance-based learning and classification
Regression and classification trees
Ensemble classifiers CUC4. Predictive models evaluation
Neural networks and Deep Learning Neural Networks

LU4. Predictive models evaluation

LU5. Develop a projet using SAS EMiner

Programs

Programs where the course is taught: