Lectures related to Design Reflection Minute

Emphasizes how pedagogical choices are embedded in technological details.

Core Skills and Curricula: Strengthening Education for the Future

Emphasizes strengthening core skills and reshaping curricula for future education.

Explores gamification, ludification, and edutainment in digital education, including games-based learning and serious games.

Active Learning for Molecular Design

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

Acquiring Data for Learning

Explores training robots through reinforcement learning and learning from demonstration, highlighting challenges in human-robot interaction and data collection.

Learning and Adaptive Control for Robots: SEDS & LPV-DS

Explores learning and adaptive control for robots through SEDS and LPV-DS, emphasizing stability, non-linear dynamics, and optimization.

Learning and Control for Complex Systems

Explores learning and control for complex systems, addressing challenges and opportunities in technology and interdisciplinary research.

Orchestration Graphs: Learning with Simulations

Explores orchestration graphs for enhancing learning with simulations and the importance of consolidating knowledge through practice.

Learning Chemical Reaction Networks

Explores sparse learning of chemical reaction networks from trajectory data using data-based methods and learning approaches.

Cognitive Reflection Test and Learning Methods

Explores learning methods, effect sizes, and cognitive assessment in education.

Digital Tools Impact on Attention and Learning

Examines the impact of digital tools on attention, learning, and cognitive load management.

Landscape and Generalisation in Deep Learning

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

Acquiring Data for Learning: Modern Approaches and Challenges

Explores modern approaches and challenges in acquiring data for learning optimal controllers through demonstrations and data-driven methods.

Collaborative Tools: Enhancing Teamwork and Learning

Explores the development of interactive tools and eye tracking technology to enhance teamwork and learning experiences.

Introduction to Machine Learning: Course Overview and Basics

Introduces the course structure and fundamental concepts of machine learning, including supervised learning and linear regression.

Introduction: Neurobotics

Introduces Neurobotics, a study combining neuroscience, robotics, and AI to understand embodied autonomous neural systems.

Machine Learning Biases

Covers the basics of machine learning, challenges in deployment, adversarial attacks, and privacy concerns.

Introducing a formal framework for representing learning

Covers Hebbian learning, reinforcement learning, types of learning, neuron models, learning rules, and weight homeostasis.

Learning in Artificial Neural Networks

Discusses the evolution of artificial neural networks, challenges in supervised learning, and the role of innate behaviors in shaping behavior.

Machine Learning for Plant Identification

Delves into challenges of supervised learning in citizen science, focusing on plant species recognition and label aggregation.