Augmenting research capacity through advanced data science methodologies, machine learning, and hands-on training for SDSU, San Diego State University Imperial Valley (SDSU IV), and community partners.
Funded by the National Institute on Minority Health and Health Disparities of the National Institutes of Health (U54MD012397), our Data Science Training Initiative develops and delivers comprehensive training programs to enhance research capacity across SDSU, SDSU IV, and community partner organizations using advanced data science methodologies including machine learning.
Hands-on training bringing together researchers from SDSU, SDSU IV, and community partner organizations.
Learn from experienced faculty using industry-standard tools like MATLAB, AutoML, and Python frameworks.
Work with real datasets and develop skills through in-class activities and hands-on exercises.
Comprehensive hands-on workshops designed to build data science and machine learning capabilities for health research applications.
The Introduction to Machine Learning Workshop is a two-day (6-hour) training that covers elementary machine learning techniques in MATLAB and AutoML, utilizing MATLAB Statistics, Machine Learning Toolbox, and Deep Learning Toolbox. Attendees will learn how to use unsupervised learning to uncover features in large datasets and supervised learning to develop prediction models through examples and in-class hands-on activities.
This two-day (6-hour) training will cover Least Squares Regression for Linear and Nonlinear Functions, Interpolation, Dimensionality reduction using PCA, LDA, and NCA, Gaussian Processes for regression and classification, Time series analysis with RNNs using LSTM and GRU architectures, and 1D CNNs. Learn to generate estimation functions, design GP models, and implement RNNs for sequential data processing.
This two-day (6-hour) workshop covers elementary machine learning techniques in MATLAB and AutoML, utilizing MATLAB Statistics, Machine Learning Toolbox, and Deep Learning Toolbox. Attendees will learn how to use unsupervised learning to uncover features in large datasets and supervised learning to develop prediction models through examples and in-class hands-on activities.
This two-day (6-hour) training will cover Least Squares Regression for Linear and Nonlinear Functions, Interpolation, Dimensionality reduction using PCA, LDA, and NCA, Gaussian Processes for regression and classification, Time series analysis with RNNs using LSTM and GRU architectures, and 1D CNNs. Learn to generate estimation functions, design GP models, and implement RNNs for sequential data processing.
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