Teaching Methodologies
The classes will be taught in a theoretical-practical format, and the teaching methodology will include various pedagogical methods, namely
the expository method, the demonstrative method, and project-based learning.
The expository method will be used to present the concepts and main contents of the curricular unit. The teacher organizes and orally
presents the content, structuring the reasoning and the result to be obtained. This presentation will be supported by slides, which will later
be made available to students. This presentation will be complemented with some provided references.
The demonstrative method will be used to exemplify some applications of concepts, namely the application of the various techniques
addressed for each task of data mining and machine learning. Based on several practical worksheets provided, the teacher shares their
know-how and demonstrates and assists students in their execution, so that they successfully perform what is requested, sometimes on
paper, other times on a computer, using a specific tool for this purpose.
The project-based learning (PBL) method will be used to build knowledge through a long and continuous study process, the purpose of
which is to address a challenge/problem whose objective is the development of a data mining and machine learning project, using data from
an organization (private or public), open data, or creating data through surveys.
Learning Results
This course unit aims to teach the fundamentals of knowledge extraction (KE) and machine learning (ML), helping to create and interpret
models that, by incorporating diverse and vast data, allow organizations to detect standard behaviours and future trends, make proactive
decisions, and identify opportunities or risks.
The main objectives are:
O1 – To explore the concepts, application domains, and opportunities of KE and ML
O2 – To present and apply in practice the main knowledge associated with the most common tasks and techniques
O3 – To develop a KE/ML project according to an appropriate methodology
The main skills to be developed are:
C1 – Ability to formulate questions that can be answered by a KE/ML project
C2 – Ability to analyse the feasibility, plan, and implement a KE/ML project
C3 – Ability to propose, create, and interpret models suitable for real-world challenges
C4 – Ability to critique current models and propose alternative models
Program
1. Introduction to Knowledge Extraction (KE) and Machine Learning (ML)
1.1 Initial Context
1.2 Knowledge Discovery in Databases
1.3 Knowledge Extraction versus Machine Learning
1.4 Related Areas
1.5 Tasks and Techniques
1.6 Open Data
2. Categories of Machine Learning
2.1 Supervised Learning
2.2 Unsupervised Learning
2.3 Reinforcement Learning
3. Predictive Activities
3.1 Classification
3.2 Regression
4. Descriptive Activities
4.1 Clustering
4.2 Summarization (and Visualization)
4.3 Association
5. CRISP-DM Methodology
6. Main Techniques
6.1 Decision Trees
6.2 Association Rules
6.3 Regression Linear with regularization
6.4 Artificial neural networks
6.5 Fuzzy sets and fuzzy logic
6.6 Bayesian networks
Internship(s)
NAO
Bibliography
Alpaydin, E. (2020). Introduction to machine learning. MIT Press.
Chakrabarti, S., et al. (2008). Data mining: Know it all. Morgan.
Chapman, P., et al. (2000). CRISP-DM 1.0: Step-by-step data mining.
Han, J., Pei, J., & Tong, H. (2023). Data mining: Concepts and techniques. Morgan Kaufmann.
Hanson, B. (2020). Machine learning: The mastery bible. Kindle Publishing.
Larose, D. T. (2005). Discovering knowledge in data: An introduction to data mining. John Wiley & Sons.
North, M. (2012). Data mining for the masses.
Tan, P.-N., Steinbach, M., Karpatne, V., & Kumar, V. (2019). Introduction to data mining. Pearson Education.
Witten, I. H., Frank, E., & Hall, M. A. (2011). Data mining: Practical machine learning tools and techniques. Morgan Kaufmann.
Yang, X.-S. (2019). Introduction to algorithms for data mining and machine learning. Pearson Education.
Zaki, M. J., & Meira, W. (2021). Data mining and machine learning: Fundamental concepts and algorithms. Cambridge University Press.