This course introduces fundamental concepts and practical skills in machine learning and deep learning. Students begin with classical techniques such as linear models and decision trees, then progress to modern architectures including Convolutional Neural Networks (CNNs), Variational Autoencoders (VAEs), Diffusion Models, and Transformers for sequence modeling.
Prerequisites: Introduction to Probability and Introduction to Computer Science.
details | course website | moodle website
This course offers an in-depth exploration of Graph Neural Networks (GNNs), covering foundational theories and state-of-the-art methodologies. Core topics include Message Passing Neural Networks (MPNNs), spectral approaches, Graph Transformers, dynamic graphs, and the interplay between large language models (LLMs) and graph structures.
Prerequisites: Foundational machine learning knowledge and proficiency in Python.
course website | moodle website
This course introduces fundamental concepts and practical skills in machine learning and deep learning. Students begin with classical techniques such as linear models and decision trees, then progress to modern architectures including Convolutional Neural Networks (CNNs), Variational Autoencoders (VAEs), Diffusion Models, and Transformers for sequence modeling.
This course offers an in-depth exploration of Graph Neural Networks (GNNs), covering foundational theories and state-of-the-art methodologies. Core topics include Message Passing Neural Networks (MPNNs), spectral approaches, Graph Transformers, dynamic graphs, and the interplay between large language models (LLMs) and graph structures.
