Lectures

Lectures


Lecture 1. “Multimodal Foundation Models” 1/4

Abstract TBA

Lecture 2. “Multimodal Foundation Models” 2/4

Abstract TBA

Lecture 3. “Multimodal Foundation Models” 3/4

Abstract TBA

Lecture 4. “Multimodal Foundation Models” 4/4

Abstract TBA



Lecture 1. “Introduction on Foundation Models” 1/5

Abstract TBA

Lecture 2. “Introduction on Foundation Models” 2/5

Abstract TBA

Lecture 3. “Introduction on Foundation Models” 3/5

Abstract TBA

Lecture 4. “Introduction on Foundation Models” 4/5

Abstract TBA

Lecture 5. “Introduction on Foundation Models” 5/5

Abstract TBA



Lecture 1. “Continuous Neural Networks / Liquid Neural Networks” 1/4

Abstract TBA

Lecture 2. “Continuous Neural Networks / Liquid Neural Networks” 2/4

Abstract TBA

Lecture 3. “Continuous Neural Networks / Liquid Neural Networks” 3/4

Abstract TBA

Lecture 4. “Continuous Neural Networks / Liquid Neural Networks” 4/4

Abstract TBA



Lecture 1. “Large Language Models: Life after Pre-training” 1/4

Abstract TBA

Lecture 2. “Large Language Models: Evaluation” 2/4

Abstract TBA

Lecture 3. “Large Language Models: Reasoning and Factuality” 3/4

Abstract TBA

Lecture 4. “Large Language Models: Retrieval augmentation and Tool-use” 4/4

Abstract TBA



Lecture 1. “Machine Learning for Modeling and Control of Complex Systems” 1/2

Abstract TBA

 

Lecture 2. “Machine Learning for Modeling and Control of Complex Systems” 2/2

Abstract TBA

Lecture 3. “Solving forward and inverse problems with and without neural networks”

Abstract TBA

Lecture 4. “Bayesian Uncertainty Quantification”

Abstract TBA



Lecture 1. “Longitudinal Language Processing with User Generated Content” 1/4

Abstract TBA

Lecture 2. “Longitudinal Language Processing with User Generated Content” 2/4

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Lecture 3. “Longitudinal Language Processing with User Generated Content” 3/4

Abstract TBA

Lecture 4. “Longitudinal Language Processing with User Generated Content” 4/4

Abstract TBA



Lecture 1. TBA 1/2

Abstract TBA

Lecture 2. TBA 2/2

Abstract TBA



Lecture 1. “Artificial Intelligence: Where We Are, Where We Are Going?” 1/3

At present, there is a great deal of confusion as to the final objective of AI. Some see Artificial General Intelligence as the ultimate and imminent goal suggesting that it can be achieved through machine learning and its further developments.

We argue that despite the spectacular rise of AI, we still have weak AI that only provides building blocks for intelligent systems, mainly intelligent assistants that interact with users in question-answer mode.
A bold step toward human-level intelligence would be the advent of autonomous systems resulting from the marriage between AI and ICT envisaged in particular by the IoT. In this evolution, the ability to guarantee the trustworthiness of AI systems – reputed to be “black boxes” very different from traditional digital systems – will determine their degree of acceptance and integration in critical applications.

We review the current state of the art in AI and its possible evolution, including:

  • Avenues for the development of future intelligent systems, in particular autonomous systems as the result of the convergence between AI and ICT;
  • The inherent limitations of the validation of AI systems due to their lack of explainability, and the case for new theoretical foundations to extend existing rigorous validation methods;
  • Complementarity between human and machine intelligence, which can lead to a multitude of intelligence concepts reflecting the ability to combine data-based and symbolic knowledge to varying degrees.

In light of this analysis, we conclude with a discussion of AI-induced risks, their assessment and regulation.

Lecture 2. TBA 2/3

Abstract TBA

Lecture 3. TBA 3/3

Abstract TBA



Lecture 1. “Deep Generative Modeling” 1/4

Introduction (Random variables, likelihood function, mixture models).

Lecture 2. “Deep Generative Modeling” 2/4

Flow-based models.

Lecture 3. “Deep Generative Modeling” 3/4

Latent Variable Models: Variational Auto-Encoders & Diffusion-based generative models.

Lecture 4. “Deep Generative Modeling” 4/4

Autoregressive Models and Transformers.



Lecture 1. “Diffusion Models” 1/3

Abstract TBA

Lecture 2. “Diffusion Models” 2/3

Abstract TBA

Lecture 3. “Diffusion Models” 3/3

Abstract TBA



Lecture 1. “Fusing Machine Learning and Optimization for Engineering” 1/3

Abstract TBA

Lecture 2. “Fusing Machine Learning and Optimization for Engineering” 2/3

Abstract TBA

Lecture 3. “Fusing Machine Learning and Optimization for Engineering” 3/3

Abstract TBA



Lecture 1. “Generalized Belief Propagation Algorithms for Tensor Network Contractions” 1/3

Abstract TBA

Lecture 2. “Generalized Belief Propagation Algorithms for Tensor Network Contractions” 2/3

Abstract TBA

Lecture 3. “Generalized Belief Propagation Algorithms for Tensor Network Contractions” 3/3

Abstract TBA




 

Tutorials


(TBA)