Knowledge Extraction and Machine Learning
Instance: 2018/2019 - 1S
Cycles of Study/Courses
||No. of Students
||Syllabus since 2009/2010
Last updated on 2018-10-01.
Fields changed: Objectives, Resultados de aprendizagem e competências, Fórmula de cálculo da classificação final, Provas e trabalhos especiais, Bibliografia Complementar, Programa, Componentes de Avaliação e Ocupação, Bibliografia Obrigatória, Obtenção de frequência
Suitable for English-speaking students
After a period in which the different companies / institutions invested significantly in data collection as part of the digitalization of their processes, there is now the need to make use of that data. The goal is to extract knowledge from data, improving efficiency and gaining competitive advantage. This is context of the Knowledge Extraction and Computational Learning (ECAC) Course (UC).
- Motivate the use of knowledge extraction (EC) from data techniques, or data mining in decision support.
- Develop the ability to properly utilize these techniques for automated analysis of large amounts of data.
- Scientific Component: 70%
- Technologycal Component: 30%
Learning outcomes and competences
Students should be able to
- Understand the different types of Data Mining (DM) tasks.
- Identify decision support problems that can be represented as DM tasks.
- Understnad the phases of a DM project.
- Know the main methods / algorithms for the most comum DM tasks and understand the basics of their behavior.
- Apply these methods to decision support problems.
- Evaluate the results of a DM project.
Pre-requirements (prior knowledge) and co-requirements (common knowledge)
- Although no particular course (UC) is required, it is useful to have attended an introductory course on statistics;
- It is also important that the student has basic knowledge of algorithms.
Descriptive Data Mining
- Introduction to knowledge extraction/data mining.
- Clustering: Partitioning-based algorithms (review of K-means, K-medoids) and hierarchical algorithms. Other algorithms. Evaluation measures.
- Association Rules: APRIORI algorithm. Other algorithms. Evaluation measures.
Predictive Data Mining
- Evaluation of predictive models: Review of decision trees. Overfitting in decision trees. Evaluation methodologies.
- Classification: Classification algorithms (rule-, instance- and kernel-based methods, Bayesian methods). Common Issues in classification (unbalanced distribution of classes and costs). Evaluation measures.
- Regression: Regression algorithms (linear and non-linear regression, regression trees, MARS). Evaluation measures.
Data Mining Projects
- Methodologies for Data Mining: The process of knowledge extraction. CRISP-DM. Project management.
- Pre-processing of data: Data cleansing and data transformation (normalization, reduction and discretization).
Analysis of Complex data
- Text mining: Representation of data for text mining. Evaluation measures.
- Web mining and recommender systems.
João Moreira, Andre Carvalho, Tomás Horvath; Data Analytics: A General Introduction
, Wiley, 2018. ISBN: 978-1-119-29626-3 (https://www.wiley.com/en-aw/A+General+Introduction+to+Data+Analytics-p-9781119296263)
Ian H. Witten, Eibe Frank; Data mining
. ISBN: 1-55860-552-5
Peter Flach; Machine Learning: The Art and Science of Algorithms that Make Sense of Data
, Cambridge University Press, 2012. ISBN: 9781107422223 (http://www.cs.bris.ac.uk/~flach/mlbook/)
Mohammed Zaki and Wagner Meira Jr.; Data Mining and Analysis: Fundamental Concepts and Algorithms
, Cambridge University Press, 2013. ISBN: 9780521766333 (http://www.dcc.ufmg.br/miningalgorithms/DokuWiki/doku.php)
Max Kuhn, Kjell Johnson; Applied Predictive Modeling
, Springer New York, 2013. ISBN: 9781461468493
Jiawei Han, Micheline Kamber; Data mining
. ISBN: 1-55860-489-8
Teaching methods and learning activities
- Theoretical presentation and discussion of the concepts.
- Laboratory sessions for practical application of the concepts learned.
The R Project for Statistical Computing
Distributed evaluation with final exam
Amount of time allocated to each course unit
|Frequência das aulas
Eligibility for exams
The distributed evaluation consists of the development of a practical project. When a student misses a component of the distributed evaluation, the grade is assigned to 0 (zero) values.
Students with Worker statute that do not go regularly to the classes should regularly discuss the evolution of their work with the lecturers, and should make their presentation, simultaneously with the ordinary students.
Calculation formula of final grade
0.5* Assignment Grade + 0.5* Exam Grade
minimum grade in each componente: 7,0 (out of 20)
Examinations or Special Assignments
The assignment will be carried out in groups of 2 students and consists in the analysis of a dataset and the preparation of a final presentation that describes and discusses the project and the corresponding results.
Special assessment (TE, DA, ...)
Students with worker statute or equivalent must take the exam and carry out the project.
Improvement of the distributed classification can only be done in the following year.