Photonic Extreme Learning Machines: Reservoir Computing Techniques

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Introduces deep learning fundamentals, covering data representations, neural networks, and convolutional neural networks.

Gradient Descent: Optimization Techniques

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

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.

Machine Learning Fundamentals

Introduces the basics of machine learning, covering supervised classification, logistic regression, and maximizing the margin.

Kernel Methods: Neural Networks

Covers the fundamentals of neural networks, focusing on RBF kernels and SVM.

Convolutional Neural Networks

Covers Convolutional Neural Networks, including layers, training strategies, standard architectures, tasks like semantic segmentation, and deep learning tricks.

Neural Network: Random Features and Kernel Regression

Covers random features in neural networks and kernel regression equivalence.

Introduction to Machine Learning

Covers the basics of machine learning, including supervised and unsupervised learning, linear regression, and classification.

Introduction to Machine Learning: Regression Fundamentals

Introduces the fundamentals of regression in machine learning, covering course logistics, key concepts, and the importance of loss functions in model evaluation.

Introduction to Machine Learning: Linear Models

Introduces linear models for supervised learning, covering overfitting, regularization, and kernels, with applications in machine learning tasks.