Fall 2023
Data Science using R
September 7 and 8, 2023
9 AM to 5 PM
G9.250A
This course would benefit students who pursue advanced R programing techniques for data science. We will provide information about key elements for data science and machine learning, including how to properly preprocess data, how to select meaningful features from the data, how to identify data clusters, and how to build a predictive model. We will then cover statistical test basics and provides semi-hands-on sessions on how to utilize the statistics for biomarker discoveries.
Day 1: Data preprocessing, Feature selection/dimensionality reduction, Data clustering, Predictive models
Day 2: Statistical test basics, Biomarker discovery I: metabolomics/proteomics data, Biomarker discovery II: RNA-seq data
Registration closed.
UTSW Graduate Students, use course number BME 5096 03
UTSW PostDocs , use course number PDRT 5095 01
Lead Instructor: Jeon Lee, PhD
Other instructors: Zachary Connerty-Marin, PhD, and Austin Marckx
Introduction to Python Software Development on GitHub
October 24 and 25, 2023
9 AM to 5 PM
G9.250A
In today's world of scientific research and development, the ability to effectively collaborate and develop software as a team is essential. This two-day introductory course is designed specifically for graduate students and postdoctoral researchers seeking to enhance their software development skills in Python and embrace modern continuous integration practices on GitHub. Participants will gain hands-on experience in using Git, pre-commit hooks, unit testing, managing dependencies, and ultimately maintaining stable code through detailed environment requirements. The skills gained in this course will enable participants to work efficiently as part of a team, ensuring the development of high-quality and maintainable software.
Registration closed.
UTSW Graduate Students, use course number BME 5096 06
UTSW PostDocs, use course number PDRT 5095 04
Lead Instructor: Kevin Dean, PhD
Other instructors: Zachary Connerty-Marin, PhD and Dushyant Mehra, PhD
Time Series Analysis
September 21 and 22, 2023
9 AM to 5 PM
G9.250A
This course aims to promote understanding of time-series data and their processing/analysis methods. Starting with an introduction to techniques for time-series data processing, we will cover analysis, modeling, and various time-series data analysis techniques being used for neural spiking data. Familiarity with R and python is required.
Day 1: Time-series signal processing (filtering, imputation, etc.), Feature extraction from time-series signals, Autocorrelation Function (ACF), AR modeling
Day 2: Neural spiking data analysis (Spike train statistics, Reverse-correlation to estimate receptive fields, Poisson neuron model, Generalized linear model)
Registration closed.
UTSW Graduate Students, use course number BME 5096 04
UTSW PostDocs, use course number PDRT 5095 02
Lead Instructor: Jungsik Noh, PhD
Other instructors: Jeon Lee, PhD and Wenhao Zhang, PhD
Architectures & Applications of Deep Learning
November 2 and 3, 2023
9 AM to 5 PM
G9.250A
Explore the driving principles behind state of the art deep neural network architectures for generative modeling with GANS (CGAN, WGAN, InfoGAN, CycleGAN), unsupervised learning with autoencoders (CVAE), image analysis (Vision Transformers and CNNs), learning from limited data (Siamese nets), and sequence learning (LSTMs). Learning objectives for this practical course are: (1) Learn to implement these architectures using leading python frameworks: TensorFlow/Keras and PyTorch, (2) Learn design patterns that increase accuracy and network understanding, (3) Learn best practices to achieve winning performance. Throughout you will learn practical applications of deep learning for prediction from a wide array of domains including tabular data and high dimensional signal, image, and video data.
Day 1: Data preprocessing, Feature selection/dimensionality reduction, Data clustering, Predictive models
Day 2: Statistical test basics, Biomarker discovery I: metabolomics/proteomics data, Biomarker discovery II: RNA-seq data
Registration closed.
Applications are open to any person at UTSW or in the surrounding community who are interested in applying deep learning solutions to their machine learning research projects. Academic credit (1 credit hour) is available.
UTSW Graduate Students, use course number BME 5096 05
UTSW PostDocs, use course number PDRT 5095 03
Lead Instructor: Albert Montillo, PhD
Other instructors: Aixa Andrade Hernandez, and Michael Holcomb