INQ0092522 - MACHINE LEARNING (MOD. B) (A) 2022-2023 - LAST DIGIT ID 0-4 - PROF. FEDERICO CHIARIOTTI
Target skills and knowledge:
aim of this course is to provide the fundamentals and basic principles
of the learning problem as well as to introduce the most common machine
learning algorithms. The course will be complemented by hands-on
The evaluation of the acquired skills and knowledge will be performed using two contributions:
1. A written exam (30 points).
2. Three simple homeworks to be done using Python (up to 3 bonus points).
1. Motivation: components of the learning problem and applications of Machine Learning. Supervised and unsupervised learning.
Introduction: The supervised learning problem, Classes of models,
Losses, Probabilistic models and assumptions on the data. Regression
3. When is a model good? Model complexity, bias variance tradeoff/generalization (VC dimension, generalization error), Cross Validation.
4. Models for Regression: Linear Regression, linear-in-the-parameters models, regularization.
5. Simple Models for Classification: Logistic Regression, Perceptron, Naïve Bayes Classifier
6. Kernel Methods: Support Vector Machines.
7. Random Forests
8. Neural Networks
9. Deep Learning: Convolutional Neural Networks, advanced models
10. Unsupervised learning: Cluster analysis, Linkage-based clustering, K-means Clustering.
11. Dimensionality reduction: Principal Component Analysis (PCA).
1. Introduction to Python
2. Linear models for regression and classification
3. Support Vector Machines
4. Neural Networks
5. Deep Learning with Keras
- Shalev-Shwartz, Shai; Ben-David, Shai, Understanding machine learning: From theory to algorithms, Cambridge University Press, 2014.
- Slides and other material will also be provided by the instructor.
- Closed: Friday, 18 November 2022, 9:51 AM