Welcome! This is the home page of the "Advanced Machine Learning" course, taking place in the winter semester of 2022/2023.
The course is part of the Master's Degree in Data Science -- Sapienza University of Rome -- organized jointly by the departments of Computer Science (DI), Information and Automation Engineering (DIAG) and Information, Electronics and Telecommunication Engineering (DIET), and Statistics (DSS).
You like the subject? why not consider a Ph.D. ? See adverts on my web-page or simply ask me!
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If you did not receive your institutional address yet due to specific situations, please request access to the group but also send an email detailing i. the circumstance; ii. the proof of your acceptance; and iii. the proof of your identity. Requests from non-instutional addresses without those cannot be accepted.
Classes started on September 22th, 2022 (in agreement with the Data Science class schedule -- link).
The course takes place @:
Room A2 (Via Ariosto, 25) on Thursdays 11:00-13:00
Room A2 (Via Ariosto, 25) on Fridays 16:00-19:00
Please also refer to the Data Science website for the class schedule.
Classes are in presence, however we make efforts to support remote participation (details distributed via the mailing lists).
Please consider Sapienza's recommendations on the attendance of lectures at this link.
Prof. Fabio Galasso, web-page, email: galasso remove_this @di DOT uniroma1 DOT it
The course will present advanced concepts of machine learning and their application in computer vision via deep neural network (DNN) models. It will include theory and practical coding, as well as a final hands-on project.
In a first part of the course, I will introduce state-of-the-art DNN models for classification, showing how to estimate which objects are within an image. I will then showcase regression, as applied to detection (where the objects are in the image), pose estimation (whether people stand, sit or crunch) and re-identification (estimating a unique vector representation for each person). I will further discuss DNNs for multi-task objectives (joint detection, pose estimation, re-identification, segmentation, depth estimation etc). This first part will include DNNs which apply to video sequences, by leveraging memory (e.g. LSTMs) or attention (Transformers).
In a second part of the course, I will discuss generalization and the effective use of labelled and unlabelled data for learning. Further to transfer learning (how pre-trained models may be deployed for other tasks), I will discuss multi-modal (with different sensor modalities such as depth or thermal cameras) and self-supervision (e.g. training the DNN model by solving jigsaw puzzles) to auto-annotate large amounts of data. Finally, I will present domain adaptation (e.g. apply daytime-detectors for night vision) and meta-learning, a most recent framework to learn how to learn a task, e.g. online or from little available data.