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Presentation
Presentation
This course aims to provide introductory skills in the field of Machine Learning, equipping students with solid, structured knowledge that will enable them to understand theoretical concepts and develop code to solve practical ML problems.
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Class from course
Class from course
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Degree | Semesters | ECTS
Degree | Semesters | ECTS
Bachelor | Semestral | 6
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Year | Nature | Language
Year | Nature | Language
2 | Mandatory | Português
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Code
Code
ULHT6634-24447
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Prerequisites and corequisites
Prerequisites and corequisites
Not applicable
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Professional Internship
Professional Internship
Não
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Syllabus
Syllabus
Introduction to Machine Learning
Machine Learning paradigms: Supervised Learning, Unsupervised Learning and Reinforcement Learning.
Data
- Types of Data
- Measures of similarity and dissimilarity
- Data normalization and visualization
- Dimensionality reduction by Principal Component Analysis
Supervised Learning
- Regression
- Decision Trees
- Artificial Neural Networks
- Support Vector Machines
- K-nearest neighbour classifier
- Methods for classifier evaluation and comparison
- Ensembles
Unsupervised Learning
- Partitional clustering
- Probabilistic clustering
- Partitional Fuzzy clustering
- Hierarchical clustering
- Clustering evaluation methods
- Other unsupervised learning topics
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Objectives
Objectives
Know
- Understand the paradigms and challenges of Automated Learning. Supervised Learning, Unsupervised Learning and Reinforcement Learning.
- Learn fundamental methods and their applications in data-driven knowledge discovery. Data, model selection, model complexity, etc.
- Understand the advantages and limitations of the Automated Learning methods studied.
Do
- Implement and adapt Machine Learning algorithms.
- Model real data experimentally
- Interpret and evaluate experimental results.
- Validate and compare Machine Learning algorithms.
Complementary skills
- Ability to assess the suitability of methods for data and practical applications.
- Ability to critically evaluate the results obtained.
- Autonomy to apply and deepen knowledge in the area of Automated Learning.
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Teaching methodologies and assessment
Teaching methodologies and assessment
- Lecturing consists of theoretical and practical classes.
- The theoretical component is essentially expository, the theory being presented together with concrete examples.
- In the practical component, practical programming problems related to the theory taught are developed and solved.
- In this course unit the evaluation includes the following elements:
- Theoretical assessment, in the form of written test, exercises, with a weight of 30% in the final grade (minimum grade: 9.5 points).
- Practical assessment (projects / programming problems / presentations), with a weight of 70% in the final grade (minimum grade: 9.5 points).
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References
References
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T. Mitchell. Machine Learning, McGraw-Hill, 1997.
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C. M. Bishop. Pattern Recognition and Machine Learning, Springer, 2006.
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Office Hours
Office Hours
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Mobility
Mobility
No
