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Introduction to Machine Learning with Python scikit-learn

Whether you're new to the field or seeking to expand your expertise, this workshop offers an exceptional opportunity to delve into supervised and unsupervised learning techniques while leveraging the cutting-edge computational resources of SMU ManeFrame 3. Dive into Scikit-Learn, a robust machine learning library, and explore its versatile tools for creating predictive models. From linear regression to support vector machines, you'll gain insights into how algorithms learn from data and make informed decisions. Venturing further, the workshop will unveil the realm of unsupervised learning, where participants will discover patterns and relationships within data without explicit labels. Leverage clustering techniques to segment data into meaningful groups and employ dimensionality reduction to distill essential information from complex datasets. Part 1: Intro to Machine Learning, scikit-learn package and data pre-processing, post-processing using evaluation metrics. Some supervised learning algorithm (regression analysis, distance-based algorithm, tree-based algorithm, Bayes algorithm, support vector machines).  Part 2: Ensemble approach in ML, Variable Selection & Dimensionality Reduction and Unsupervised Learning.

 

Presented by Tue Vu

Date:
Thursday, October 3, 2024
Time:
1:00pm - 4:00pm
Hosted by:
OIT Research and Data Science Services
Space:
Fondren Library 109
Audience:
  Faculty/staff     Graduates     Undergraduates  
Categories:
  Scholarship and Research  
Registrations open at 1:00pm Thursday, September 26, 2024

Any person who requires a reasonable accommodation on the basis of a disability in order to participate in this program should contact presenter at tuev@smu.edu at least one week prior to the event to arrange for the accommodation.

Location