Contribution of the course to the overall degree programme goals
The goal of this course is to teach students to analyse, to classify, to index, to rank and to build recommender systems for web content.
Expected learning outcomes
The course discusses fundamental technique for predictive and descriptive data analysis, with focus on Web data.
Students will achieve the following learning outcomes:
Knowledge and understanding: i) understanding principles of non-supervised learning; ii) understanding principles of supervised learning; iii) understanding principled of web content mining.
Applying knowledge and understanding: i) being able to apply supervised and unsupervised analysis techniques; ii) being able to use data analysis software tools (e.g., scikit-learn).
Making judgements: i) being able to choose the most appropriate to a given problem and to evaluate its performance.
Communication: i) reporting comprehensive comparative analysis among different data analysis methods
Students should have achieved the learning outcomes of courses "Programming" and "Probability and Statistics"
(even without passing the corresponding exams).
- Knowledge Discovery in Databases
- Classification: nearest neighbor, naive bayes, decision trees, ensemble methods.
- Clustering: similarity based, density based, graph based.
- Web Mining: graph mining, recommendation, collaborative filtering.
Lecture notes. Selected readings provided during the course.
Learning outcomes are verified by a set of exercises and a project.
The exercises require to apply data analysis methods to a given dataset of limited complexity.
The project requires to conduct a comparative analysis of different tools applied to a specific dataset or problem.
The student must chose and motivate the most appropriate solution and deliver a report discussing a comparative analysis of the chosen methods.
Lectures and hands-on sessions.
Last update of the programme