Learning From Machines

Overview

Join us in exploring the fascinating intersection of neuroscience and machine learning. This event delves into understanding human cognition, perception, and consciousness, offering insights through innovative computational models and compelling visual metaphors.

Presented by YouTube, this unique learning opportunity falls within the domains of Artificial Intelligence Courses and Conference Talks. Whether you're an AI enthusiast or a neuroscience researcher, broaden your knowledge and experience the convergence of these cutting-edge fields.

Don't miss out on this opportunity to learn from the machines shaping our future.

Syllabus

• Introduction to Machine Learning and Neuroscience
Overview of machine learning principles
Basics of neuroscience relevant to AI
• Neural Networks and the Brain
Comparison of biological neurons and artificial neurons
Structure and function of neural networks
Hebbian learning and synaptic plasticity
• Machine Learning Models of Cognition
Computational models of human learning
Memory systems and neural computation
Reinforcement learning parallels with human decision-making
• Perception and Sensory Processing
Visual processing in brains and machines
Representation and interpretation of sensory information
Convolutional neural networks and visual perception
• Consciousness and Machine Intelligence
Theories of consciousness
Potential for machine consciousness
Ethical implications of conscious machines
• Emulating Human Perception
Developing visual metaphors in machine learning
Bridging human perception and computational models
Applications in computer vision and image recognition
• Advanced Topics in Neuro-AI
Brain-inspired architectures and algorithms
Neuromorphic computing
Future directions in AI and neuroscience integration
• Project and Case Studies
Analyzing real-world applications of neuro-AI
Designing and implementing a simple neuro-inspired model
Evaluating case studies of machine learning in neuroscience research
• Conclusion and Future Perspectives
Summarizing key insights
Discussing open questions and challenges
Exploring future research pathways in neuro-AI integration

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Subjects

Conference Talks