Lectures related to Sensory learning with neural networks

Brain Intelligence: Continual Learning of Representational Models

Delves into the continual learning of representational models after deployment, highlighting the limitations of current artificial neural networks.

Biological and non-biological bodies

Explores the components of neuro robots and compares them to actual animals.

Introduction to Systems Neuroscience: Memory Systems Overview

Introduces systems neuroscience, focusing on neural circuits, memory systems, and course logistics.

Computer Vision: Historical Insights and Project Inspirations

Explores the historical development of computer vision and inspires innovative project ideas.

Theory of reinforcement learning: Introductory question

Covers the theory of reinforcement learning, exploring exploration/exploitation dilemma and continuous state/action spaces.

Neural Taskonomy and Historical Perspectives in Visual Intelligence

Covers Neural Taskonomy, the evolution of neural networks, and historical perspectives in visual intelligence.

Introduction to synaptic plasticity

Covers synaptic plasticity, types of synapses, Hebb's postulate, LTP, LTD, and network oscillations in the hippocampus.

Neuroscience and Machine Learning: Understanding Visual Intelligence

Explores the relationship between neuroscience and machine learning in visual intelligence.

Neural Networks: Multilayer Perceptrons

Covers Multilayer Perceptrons, artificial neurons, activation functions, matrix notation, flexibility, regularization, regression, and classification tasks.

Neural Networks: Regression and Classification

Explores neural networks for regression and classification tasks, covering training, regularization, and practical examples.

Computer Vision History Recap

Offers a historical overview of computer vision, exploring key developments and influential figures in the field.

Perception and Action: Anatomic Conventions, Homunculus, Body Scheme

Explores anatomic conventions, homunculus mapping, and body scheme integration in perception and action.

Neural Networks: Basics and Applications

Explores neural networks basics, XOR problem, classification, and practical applications like weather data prediction.

Neuromorphic Computing: Concepts and Hardware Implementations

Covers neuromorphic computing, challenges in ternary and binary computing, hardware simulations of the brain, and new materials for artificial brain cells.

Feedback Mechanisms in Visual Intelligence and Adaptation

Explores the significance of feedback mechanisms in visual intelligence and adaptation processes.

Neural Networks: Training and Activation

Explores neural networks, activation functions, backpropagation, and PyTorch implementation.

Neural Networks: Perceptron Model and Backpropagation Algorithm

Covers the perceptron model and backpropagation algorithm in neural networks.

Hopfield Model: Memory and Dynamics

Explores the Hopfield Model for associative memory and its dynamics.

Deep Learning: Data Representations and Neural Networks

Covers data representations, Bag of Words, histograms, data pre-processing, and neural networks.

Memory & Learning

Explores memory, learning, cognitive load, and problem-solving strategies to enhance learning and cognitive performance.