The 6th Advanced Course on Data Science & Machine Learning – ACDL 2023 (June 10-14) is a full-immersion five-day Course at the Riva del Sole Resort & SPA (Castiglione della Pescaia – Grosseto – Tuscany, Italy) on cutting-edge advances in Data Science and Deep Learning Learning with lectures delivered by world-renowned experts. The Course provides a stimulating environment for junior and senior academics, early career researches, Post-Docs, PhD students and industry leaders. Participants will also have the chance to present their results with talks, and to interact with their colleagues, in a convivial and productive environment.

MSc students, PhD students, PostDocs, Industry Practitioners, Junior and Senior Academics, and will be typical profiles of the ACDL attendants.The Course will involve a total of 36–40 hours of lectures, according to the academic system the final achievement will be equivalent to 8 ECTS points for the PhD Students and the Master Students attending the Course.

LECTURERS:
Each Lecturer will hold up to four lectures on one or more research topics.
https://acdl2023.icas.cc/lecturers/

Luca Beyer, Google Brain, Zürich, Switzerland 
Lecture 1: "Large-Scale Pre-Training & Transfer in Computer Vision and Vision-Text Models 1/2"
Lecture 2: "Large-Scale Pre-Training & Transfer in Computer Vision and Vision-Text Models 2/2"
Lecture 3: "Transformers 1/2"
Lecture 4: "Transformers 2/2"

Aakanksha Chowdhery, Google Brain, USA     
Lecture 1: "PaLM-E: An Embodied Language Model"
Lecture 2: "Efficiently Scaling Large Model Inference"

Thomas Kipf, Google Brain, USA 
Lecture 1: "Graph Neural Networks 1/2"
Lecture 2: "Graph Neural Networks 2/2"
Lecture 3: "Structured Representation Learning for Perception 1/2"
Lecture 4: "Structured Representation Learning for Perception 2/2"

Pushmeet Kohli, DeepMind, London, UK                 
Lectures: TBA

Yi Ma, University of California, Berkeley, USA
Lecture 1: "An Overview of the Principles of Parsimony and Self-Consistency: The Past, Present, and Future of Intelligence"
Lecture 2: "An Introduction to Low-Dimensional Models and Deep Networks"
Lecture 3: "Parsimony: White-box Deep Networks from Optimizing Rate Reduction"
Lecture 4: "Self-Consistency: Closed-Loop Transcription of Low-Dimensional Structures via Maximin Rate Reduction"

Gerhard Paass, Fraunhofer Institute - IAIS, Germany
Lecture 1: "Introduction to Foundation Models"
Lecture 2: "Foundation Models for Retrieval Applications"
Lecture 3: "Combining Foundation Models with External Text Resources"
Lecture 4: "Approaches to Increase Trustworthiness of Foundation Models2

Panos Pardalos, University of Florida, USA
Lecture : "Diffusion capacity of single and interconnected networks"

Qing Qu, University of Michigan, USA
Lecture 1: "Low-Dimensional and Nonconvex Models for Shallow Representation Learning"
Lecture 2: "Low-Dimensional Structures in Deep Representation Learning I"
Lecture 3: "Low-Dimensional Structures in Deep Representation Learning II"
Lecture 4: "Robust Learning of Overparameterized Networks via Low-Dimensional Models"


Zoltan Szabo, LSE, London, UK 
Lecture 1: "Shape-Constrained Kernel Machines and Their Applications"
Lecture 2: "Beyond Mean Embedding: The Power of Cumulants in RKHSs"

Michal Valko, DeepMind Paris & Inria France & ENS MVA
Lecture 1: "Reinforcement learning"
Lecture 2: "Deep Reinforcement Learning"
Lecture 3: "Learning by Bootstrapping: Representation Learning"
Lecture 4: "Learning by Bootstrapping: World Models"


TUTORIAL SPEAKERS:
Each Tutorial Speaker will hold more than four lessons on one or more research topics.

Bruno Loureiro, École Normale Supérieure, France 
Lectures 1-10: "Wonders of high-dimensions: the maths and physics of Machine Learning"
              
Varun Ojha, Newcastle University, UK
Lecture 1: "Characterization of Deep Neural Networks"
Lecture 2: "Backpropagation Neural Tree"
Lecture 3: "Sensitivity Analysis of Deep Learning and Optimization Algorithms"

https://acdl2023.icas.cc/lecturers/

PAST LECTURERS: https://acdl2023.icas.cc/past-lecturers/