Adavanced Machine Learning


Welcome! This is the home page of the "Advanced Machine Learning" course, taking place in the summer semester of 2019/2020.

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!

News 09/03/2020: Lecture would resume this week at regular times but be remote. So there would be lectures from Thursday March 12th. Further instructions on means to connect would be provided soon via the course mailing list.


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Time and rooms

The course takes place @:

  • Room 13 (Tumminelli building -- CU007 map) on Thursdays 14:00-16:00
  • Room 14(Tumminelli building -- CU007 map) on Fridays 12:00-16:00

Link to the official lecture timetable.


Prof. Fabio Galasso, web-page, email: galasso remove_this @di DOT uniroma1 DOT it


The course would present advanced concepts of machine learning and their application in computer vision via deep neural network (DNN) models. It would include theory and practical coding, as well as a final hands-on project.

In a first part of the course, I would introduce state-of-the-art DNN models for classification, showing how to estimate which objects are within an image. I would 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 would further discuss DNNs for multi-task objectives (joint detection, pose estimation, re-identification, segmentation, depth estimation etc). This first part would include DNNs which apply to video sequences, by leveraging memory (e.g. LSTMs) or attention (Transformers).

In a second part of the course, I would 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 would 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 would 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.