Discovering Composable Modules in Meta-learning

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Explores bug-finding, verification, and the use of learning-aided approaches in program reasoning, showcasing examples like the Heartbleed bug and differential Bayesian reasoning.

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Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.

Autonomous Vehicles: Intelligence and Perception

Explores intelligence, perception, and AI applications in autonomous vehicles, emphasizing rational thinking and social intelligence.

Machine Learning: Supervised and Unsupervised Learning Techniques

Covers supervised and unsupervised learning techniques in machine learning, highlighting their applications in finance and environmental analysis.

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Provides an overview of Machine Learning, including historical context, key tasks, and real-world applications.

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Explores the challenges and insights of deep learning, focusing on loss landscape, generalization, and feature learning.

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Explores the EPFL Alice unit's role in shaping machine learning and AI in Europe, focusing on research advancements and collaboration within the AI community.

Deep Learning: Fundamentals

Covers the fundamentals of deep learning, from artificial neurons to modern networks.

Financial Time Series Analysis

Covers stylized facts of asset returns, summary statistics, testing for normality, Q-Q plots, and efficient market hypothesis.