Lectures related to Kernel Methods: Neural Networks

Neural Networks: Multilayer Perceptrons

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

Neural Networks: Random Features and Kernel Regression

Explores random features in neural networks and kernel regression using stochastic gradient descent.

Neural Networks: Regression and Classification

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

Neural Networks: Multilayer Learning

Covers the fundamentals of multilayer neural networks and deep learning, including back-propagation and network architectures like LeNet, AlexNet, and VGG-16.

Kernel Methods: SVM and Regression

Introduces kernel methods like SVM and regression, covering concepts such as margin, support vector machine, curse of dimensionality, and Gaussian process regression.

Neural Networks: Basics and Applications

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

Introduction to Machine Learning: Supervised Learning

Introduces supervised learning, covering classification, regression, model optimization, overfitting, and kernel methods.

Deep Learning Fundamentals

Introduces deep learning, from logistic regression to neural networks, emphasizing the need for handling non-linearly separable data.

Deep Learning: Multilayer Perceptron and Training

Covers deep learning fundamentals, focusing on multilayer perceptrons and their training processes.

Deep Learning: Data Representations and Neural Networks

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

Feature Selection, Kernel Regression, Neural Networks Playground

Covers feature selection, kernel regression, and neural networks through exercises.

Neural Networks: Training and Activation

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

Optimization in Machine Learning: Gradient Descent

Covers optimization in machine learning, focusing on gradient descent for linear and logistic regression, stochastic gradient descent, and practical considerations.

Multilayer Neural Networks: Deep Learning

Covers the fundamentals of multilayer neural networks and deep learning.

Neural Network: Random Features and Kernel Regression

Covers random features in neural networks and kernel regression equivalence.

Kernel Regression: Basics and Applications

Explores kernel regression, the curse of dimensionality, and random features in neural networks.

Kernel Methods and Regression

Covers kernel methods, kernel regression, RBF kernel, and SVM for classification.

Multi-layer Neural Networks

Covers the fundamentals of multi-layer neural networks and the training process of fully connected networks with hidden layers.

Neural Networks Recap: Activation Functions

Covers the basics of neural networks, activation functions, training, image processing, CNNs, regularization, and dimensionality reduction methods.

Gradient Descent: Optimization Techniques

Explores gradient descent, loss functions, and optimization techniques in neural network training.