Lectures related to Cognitive Reflection Test and Learning Methods

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.

Spatial Configuration and Teaching

Explores the impact of spatial configuration on teaching interactions in classrooms, analyzing seating arrangements and teacher movements.

Proper Actions and Quotients

Covers proper actions of groups on Riemann surfaces and introduces algebraic curves via square roots.

Landscape and Generalisation in Deep Learning

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

How People Learn: Design and Learning Process

Covers observational learning, implicit pattern learning, inquiry learning, procedural learning, and working memory functions.

Acquiring Data for Learning

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

Introduction to Machine Learning: Course Overview and Basics

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

Active Learning for Molecular Design

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

Neuroscience and AI: Bridging the Gap

Explores the gap between AI and human intelligence through neuroscience-inspired models and algorithms.

Cognitive Processes: Learning and Thinking

Covers teaching approaches, cognitive processes, and learning strategies, including left-brain thinking, concept mapping, and cooperative learning significance.

Deep and Convolutional Networks: Generalization and Optimization

Explores deep and convolutional networks, covering generalization, optimization, and practical applications in machine learning.

Interactive Learning Strategies: Active vs Passive Learning

Explores active and passive learning strategies, brain activity, memory, and concept maps.

Learning and Control for Complex Systems

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

Deep Learning: Convolutional Networks

Explores convolutional neural networks, backpropagation, and stochastic gradient descent in deep learning.

Neural Machine Translation

Explores the evolution and challenges of Neural Machine Translation systems and the evaluation metrics used in this field.

Deep Visual Recognition: Interpretability

Explores deep visual recognition, interpretability, CNN architectures, visual dictionaries, and attention mechanisms.

Learning and Adaptive Control for Robots

Explores learning, adaptive control, cutting soft tissue, and robotic implementation for robots.

Advanced Machine Learning: Brief review of C-SVM

Covers clustering, classification, and Support Vector Machine principles, applications, and optimization, including non-linear classification and Gaussian kernel effects.

Growth rate and uniform convergence

Explores growth rate, uniform convergence, PAC learning, and distribution learning challenges.

Effective Note Taking: Laptop vs. Handwritten

Compares longhand and laptop note taking for learning, emphasizing the detrimental effects of verbatim transcription on understanding and retention.