Introduces a programming course for engineers, emphasizing the importance of mastering multiple languages for future projects.
Covers key concepts in reinforcement learning, neural networks, clustering, and unsupervised learning, emphasizing their applications and challenges.
Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.
Covers Gaussian Mixture Models, Denoising, Data Classification, and Spike Sorting using Principal Component Analysis.
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 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.
