Machine Learning Programming: Introduction

Related lectures (30)

Page 2 of 3

Introduces artificial neural networks, CNNs, and dimensionality reduction using PCA and autoencoders.

Covers programming concepts using MATLAB, C, and LabVIEW for engineering projects, including a billiards game analysis.

Introduces artificial neural networks and explores various dimensionality reduction techniques like PCA, LDA, Kernel PCA, and t-SNE.

Covers the application of Principal Component Analysis in facial recognition using a famous faces dataset.

Covers fundamental programming concepts such as algorithms, variables, and expressions in C++.

Covers key concepts and examples of machine learning algorithms and techniques.

Covers the analysis of air pollution data, focusing on R basics, visualizing time series, and creating summaries of pollutant concentrations.

Covers programming basics for engineers, emphasizing MATLAB, C, and LabVIEW tools for project development.

Covers stylized facts of asset returns, summary statistics, testing for normality, Q-Q plots, and efficient market hypothesis.

Covers programming concepts, memory management, debugging, and advanced MATLAB functionalities.