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 2023
- Offered
- 2023/24
- 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 machine learning. Supervised semi-supervised and unsupervised approaches.
Classification, clustering, anomaly detection. Application to computer vision and natural language processing. Introduction to deep learning. Advanced neural network models. - 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. - Prerequisites
- None
- 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 machine learning. Supervised, semi-supervised and unsupervised approaches.
Data classification (decision trees, rule-based approach, naive bayes, nearest neighbors, SVM, neural networks, ensemble) , clustering (k-means, hierarchical approaches, density based approaches), anomaly detection. Applications to computer vision and natural language processing. Introduction to deep learning. Advanced neural networks models (convolutional NN, recurrent NN, generative models).