Lectures related to Transfer learning

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

Neural Networks: Perceptron Model and Backpropagation Algorithm

Covers the perceptron model and backpropagation algorithm in neural networks.

Visual Intelligence and Learning: Insights and Applications

Explores visual intelligence, robotics perception, and multi-task learning techniques in computer vision.

Convolutional Networks: Applications beyond Object Recognition

Delves into the applications of convolutional networks beyond object recognition, emphasizing their impact on neuroscience, brain sciences, and art.

Chemical Reaction Optimization: Multi-Task Learning

Explores multi-task learning for accelerated chemical reaction optimization, showcasing challenges, automated workflows, and optimization algorithms.

Gaussian Discriminant Rule: Classification & Boundaries

Explores the Gaussian Discriminant Rule for classification using Gaussian Mixture Models and discusses drawing boundaries and model complexity.

Neuromorphic Paradigm: Quasi-Digital and Neuromorphic Signals

Explores the integration of quasi-digital and neuromorphic signals for efficient information transmission in biomedical engineering applications.

Introduction to Machine Learning

Provides an overview of Machine Learning, including historical context, key tasks, and real-world applications.

PCA: Interactive class

On PCA includes interactive exercises and emphasizes minimizing information loss.

Kernel K-Means: Convergence Proof

Explores the Kernel K-Means algorithm, convergence proof, RBF kernel influence, and clustering interpretation.

Deep Learning for Autonomous Vehicles: Learning

Explores learning in deep learning for autonomous vehicles, covering predictive models, RNN, ImageNet, and transfer learning.

Modern Convolutional Networks and Image Recognition

Explores the evolution of deep convolutional networks and their impact on image recognition accuracy.

Neural Networks Optimization

Explores neural networks optimization, including backpropagation, batch normalization, weight initialization, and hyperparameter search strategies.

Support Vector Machines: Theory and Applications

Explores Support Vector Machines theory, parameters, uniqueness, and applications in machine learning.

Self-Supervised Learning: State of the Art

Explores self-supervised learning, transfer learning, SSL prediction tasks, feature learning, image rotations, contrastive learning, and vision learners.

Neural Taskonomy and Historical Perspectives in Visual Intelligence

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

Bullet Arm: Robotic Manipulation Benchmark

Introduces BulletArm, an open-source robotic manipulation benchmark and learning framework, covering design goals, benchmark tasks, and learning algorithms.

Feedforward Neural Networks: Activation Functions and Backpropagation

Introduces feedforward neural networks, activation functions, and backpropagation for training, addressing challenges and powerful methods.

Statistical Learning Theory: Conclusions on Deep Learning

Covers the conclusions on deep learning and an introduction to statistical learning theory.

Transfer Learning with CNNs

Explores transfer learning with CNNs, fine-tuning, and network depth impact.