Unit MACHINE LEARNING
- Course
- Informatics
- Study-unit Code
- A002051
- Curriculum
- In all curricula
- Teacher
- Valentina Poggioni
- Teachers
-
- Valentina Poggioni
- Hours
- 68 ore - Valentina Poggioni
- CFU
- 9
- Course Regulation
- Coorte 2024
- Offered
- 2024/25
- Learning activities
- Caratterizzante
- Area
- Discipline informatiche
- Academic discipline
- INF/01
- Type of study-unit
- Obbligatorio (Required)
- Type of learning activities
- Attività formativa monodisciplinare
- Language of instruction
- INGLESE
- Contents
- Introduction to deep learning. Advanced neural network models: CNN, LSTM, GRU.
Non-supervised learning: clustering, Anomaly detection, Dimensionality Reduction and Representation Learning.
Generative models. Attention Mechanism and Transformers.
Introduction to Adversarial Machine Learning. - Reference texts
- Tan, Steinbach, Karpatne, Kumar.
Introduction to Data Mining, 2nd Edition
2019. Pearson
Charu C. Aggarwal.
Neural Networks and Deep Learning: a text book. Springer 2018 - Educational objectives
- The student will know the fundamentals and most important machine learning methods based on the supervised, semi-supervised and unsupervised approach.
He/she will know also some of the most known methods of deep learning , with a particular focus on computer vision and natural language processing application. He/she will able to design, implement, train and test intelligent systems for data classification, clustering and anomaly detection based on advanced neural network models.
He/she will know the main generative approaches, the attention mechanism and the transformer architecture that is at the base of modern Large Language Models - Prerequisites
- Suggested courses:
"Introduction to artificial intelligence" e "ARTIFICIAL INTELLIGENT SYSTEMS (1 semester)" - Teaching methods
- In-class lessons and hands-on in computer lab
- Other information
- For any other information www.unistudium.unipg.it
- Learning verification modality
- Oral exam and final project. Intermediate exams during the course
- Extended program
- Introduction to deep learning. Advanced neural network models: CNN, LSTM, GRU.
Non-supervised learning: clustering, Anomaly detection, Dimensionality Reduction and Representation Learning.
Generative models. Attention Mechanism and Transformers.
Introduction to Adversarial Machine Learning.