Environmental Computational Science: Earth Observation

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Landscape and Generalisation in Deep Learning

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

Active Learning for Molecular Design

Covers machine learning approaches for material design, practical examples, and software tools for research.

Introduction to Reinforcement Learning: Concepts and Applications

Introduces reinforcement learning, covering its concepts, applications, and key algorithms.

Reinforcement Learning: Policy Gradient and Actor-Critic Methods

Provides an overview of reinforcement learning, focusing on policy gradient and actor-critic methods for deep artificial neural networks.

Machine Learning Fundamentals

Covers the fundamental principles and methods of machine learning, including supervised and unsupervised learning techniques.

Learning-aided Program Reasoning

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

Plastic Pollution: Challenges and Solutions

Explores the impact of plastic pollution in oceans and discusses solutions to address the crisis through innovative projects and waste management strategies.

Artificial Intelligence: The New Radical Addition to Work

Explores the convergence of IoT, Big Data, and Deep Learning in Artificial Intelligence applications across various industries.

Bagging: Regularization Method in Deep Learning

Explores bagging as a regularization method in deep learning, training multiple model variants on different data subsets to improve generalization.

Visual Intelligence: Machines and Minds

Explores visual intelligence, evolution of eyes, blind spots, biomimicry, and the two-way connection between natural and artificial systems.