Objectives
General goals:
The course introduces motivations, paradigms and applications of machine learning. It is to be considered an introductory course.
Specific goals:
Supervised learning: decision trees, instance-based learning, naïve Bayes, support vector machine, neural networks, deep learning, ensamble methods. Unsupervised learning: clustering, association rules. Semi-supervised learning. Reinforcement learning. Genetic algorithms and genetic programming. Issues in machine learning: underfitting, overfitting, model selection, error analysis.
Knowledge and understanding:
Students will learn which algorithms best fit which categories of problems, how to describe the application domain, how to tune parameters and hyperparameters of the model, how to test performances.
Applying knowledge and understanding:
Students will experiment the main algorithms using the Weka ML software Weka, Tensor Flow or scikit-learn.
Critical and judgmental skills:
Students will be able to will understand the categories of problems that can be efficiently solved with machine learning algorithms, under which conditions.
Communication skills:
These will be tested during written exams and project reporting.
Ability of learning:
Students will receive a solid basis for further deepening of more advanced methods, such as deep learning, probabilistic learning, and others
Channels
NESSUNA CANALIZZAZIONE
PAOLA VELARDI Teacher profile
Programme
Topics Supervised learning: decision trees, instance-based learning, naïve Bayes, support vector machine, neural networks, deep learning, ensamble methods. Unsupervised learning: clustering, association rules. Semi-supervised learning. Reinforcement learning. Genetic algorithms and genetic programming. Issues in machine learning: underfitting, overfitting, model selection, error analysis.
Adopted texts
see http://twiki.di.uniroma1.it/twiki/view/ApprAuto
Bibliography
http://twiki.di.uniroma1.it/twiki/view/ApprAuto
Prerequisites
Basic notions of logic and algorithms - python and/or java
Exam modes
Mid-term, final written test, project
Exam reservation date start | Exam reservation date end | Exam date |
---|---|---|
01/01/2020 | 28/01/2020 | 29/01/2020 |
19/04/2020 | 29/04/2020 | 30/04/2020 |
20/05/2020 | 14/06/2020 | 15/06/2020 |
28/07/2020 | 28/07/2020 | 29/07/2020 |
15/08/2020 | 10/09/2020 | 11/09/2020 |
- Academic year: 2019/2020
- Curriculum: Multimedia Computing and Interaction
- Year: First year
- Semester: First semester
- SSD: INF/01
- CFU: 6
- Attività formative caratterizzanti
- Ambito disciplinare: Discipline Informatiche
- Exercise (Hours): 36
- Lecture (Hours): 24
- CFU: 6.00
- SSD: INF/01