Advanced Machine Learning
( and Computer Vision)

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Time and rooms

Classes starts on September, 22th (in agreement with the Data Science and Computer Science class schedule).

The course takes place @:

• Room A2 (Via Ariosto, 25) on Mondays 13:00-16:00

• Room A2 (Via Ariosto, 25) on Wednesdays 12:00-14:00

Please also refer to the Data Science and Computer Science first-semester lecture times for the class schedule.

Classes are in presence.

Lecturer

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

Programme

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), extending to long-term modelling with State-Space-Models (SSMs) and to modelling sequences of structured data with Graph Convolutional Networks.

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, with text and sound) and self-supervision (e.g. training the DNN model by leveraging time and causality in time) to auto-annotate large amounts of data. Also, 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, and continual learning. I will introduce novel machine learning trends such as hyperbolic neural networks, showcasing their use for estimating uncertainty and enabling the model to request the intervention of humans and active learning.  Finally, I will discuss generative AI techniques such as VAE, GANs and diffusion models, and their applications to tasks such as anomaly detection while estimating the model uncertainty.

Pre-requisites

The students' expertise will be assessed at the first two lectures, to set the course at the most advanced possible starting point.

The following are pre-requisites which attendants should possess at the course start, or be ready to remedy with own study:

• Proficiency in Python, some experience with Pytorch

• Calculus, Linear Algebra, Probability

  • Taking derivatives, understanding matrix vector operations and notation

• Machine Learning

  • Regression, binary and multi-class classification

  • Cost functions, derivatives and optimization with gradient descent

• Basics of Deep Learning

  • Basics of fully-connected and convolutional networks

  • Basics of backprop

Suggestions and material for own study to remedy pre-requites will be discussed upon request.