General information |
Course unit name: Introduction to Machine Learning
Course unit code: 569389
Academic year: 2021-2022
Coordinator: Maria Salamo Llorente
Department: Department of Mathematics and Computer Science
Credits: 5
Single program: S
Estimated learning time |
Total number of hours 125 |
Face-to-face and/or online activities |
60 |
(Due to COVID-19 restrictions, we expect to have 50%-75% of in-person activities) |
Independent learning |
65 |
Teaching blocks |
1. Unsupervised Learning
1.1. Introduction to unsupervised learning
1.2. Cluster analysis
1.3. Factor Analysis
1.4. Visualization
2. Supervised learning
2.1. A gentle introduction to supervised learning
2.2. Lazy Learning
2.3. Feature selection
2.4. Model Selection
2.5. Support Vector Machine
2.6. Recommender Systems
Teaching methods and general organization |
Teaching will follow a face-to-face (in-person), virtual (online), or mixed model according to the instructions of the competent authorities. In principle, we expect to follow the mixed teaching model for the 2021-2022 academic year.
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Official assessment of learning outcomes |
Depending on the health situation, evaluable activities can be face-to-face tests, synchronous online tests, or work delivery.
Examination-based assessment Depending on the health situation, evaluable activities can be face-to-face tests, synchronous online tests, or work delivery.
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