Artificial Neural Networks and Deep Learning

About this course

Artificial Neural Networks are programs that write themselves when given an objective, some training data, and abundant computing power. Recently, these programs have brought about a wide array of impressive innovations, such as self-driving cars, face recognition, and human-like speech generators. This course offers you an introduction to Deep Artificial Neural Networks (i.e. “Deep Learning”). With focus on both theory and practice, we cover models for various applications, how they are trained and tested, and how they can be deployed in real-world applications.

Pre-requisites

One year of computer science at university level.

One of the computer science courses should be in algorithms and data structures.

One course in either probability theory, linear algebra, or statistics at university level. Knowledge of at least one object-oriented programming language (e.g. Java/Python).

Faculty

Lorenzo Belgrano

Faculty

M.Sc. in Mathematical Modelling and Computation, Technical University of Denmark (2019). B.Sc. in Information Engineering, University of Padua (2016). Senior Machine Learning Engineer at Corti (2019–2024). Previous experience implementing deployment pipelines using Docker and Azure ML and developed a FastAPI to request detection to ML models using Tensorflow Serving. With DIS since 2023.