Linear Classification: Parameterizing Lines and Distance Calculation

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

Linear Regression: Basics

Covers the basics of linear regression, binary and multi-class classification, and evaluation metrics.

Multi-layered Perceptron: History and Training Algorithm

Explores the historical development and training of multi-layered perceptrons, emphasizing the backpropagation algorithm and feature design.

Neural Networks: Training and Activation

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

Binary Classification by Regression: Decision Functions and Cost Functions

Explores binary classification by regression, decision functions, and various cost functions.

Introduction to Learning by Stochastic Gradient Descent: Simple Perceptron

Covers the derivation of the stochastic gradient descent formula for a simple perceptron and explores the geometric interpretation of classification.

The Problem of Overfitting

Discusses the problem of overfitting in deep networks and the importance of controlling flexibility to avoid it.

Generalized Linear Regression: Classification

Explores Generalized Linear Regression, Classification, confusion matrices, ROC curves, and noise in data.

Photonic Extreme Learning Machines: Reservoir Computing Techniques

Covers photonic extreme learning machines and reservoir computing, focusing on their architectures, programming techniques, and applications in optical computing.

Logistic Regression: Interpretation & Feature Engineering

Covers logistic regression, probabilistic interpretation, and feature engineering techniques.