Labs for the Machine Learning bachelor course @ USI SP 23
This repository contains the code files for the Machine Learning Bachelor Course lab sessions for the Spring 2023 semester. Here, you will find the code used in each lab session, organised by topic and lab number.
Course director: Alippi Cesare
Assistants:
Course description: The course will address the following topics: Supervised learning: linear and nonlinear models for regression and prediction, statistical theory of learning, feature extraction and model selection. Deep learning: architectures including autoencoders, convolutional neural networks and learning procedures. Model performance assessment: cross-validation, k-fold cross-validation, leave-one-out, bootstrap. Unsupervised learning: K-means clustering, fuzzy C-means, principal component analysis.
Course objectives: Students will learn how to design linear and nonlinear models for regression, prediction and classification as well as soundly assess their performance. At the same time, students will learn how to use deep learning architectures and learning algorithms in key real-world applications.
Learning methods: Lab sessions will focus on practical aspects and show how to design an appropriate machine learning solution to real-world problems. Basics in Calculus, Probability and Statics are requested.
Bibliography:
- Gori, Marco. Machine learning: a constraint-based approach. Cambridge, MA: Morgan Kaufmann Publishers, 2018.
- Hastie, Trevor J., Tibshirani, Robert, Friedman, Jerome. The elements of statistical learning: data mining, inference, and prediction. 2nd ed. [corrected at 5th printing]. New York, N.Y.: Springer, 2011.
- James, Gareth, Witten, Daniela, Hastie, Trevor, Tibshirani, Robert. An introduction to statistical learning: with applications in R. Second edition. New York: Springer, 2021.
The code files are provided for your reference and learning purposes. Please note that they are not meant to be copied or shared for any form of academic misconduct.
You can download the code files directly from GitHub, or clone the entire repository to your local machine.
This repository is maintained by the course teaching assistants. If you notice any issues with the code files or have any suggestions for improvements, please feel free to create an issue or pull request.
If you have any questions or concerns about the course or the code files, please contact the course teaching assistants.
We hope this repository will help you in your learning journey and support your success in the course. Happy coding!