Machine Learning Hybrid Course


This Machine Learning Workshop will explore the fundamentals of applied supervised and unsupervised methodologies in machine learning.  Through a case-study and applied hands-on approach, students will learn various machine learning libraries within R in order to extract patterns and design ML models for predictive analysis.

Schedule / Agenda

Day 1 (Sunday May 12th 2019) - Overview of Machine Learning ( Virtual)
  • Introduction
  • Machine Learning Motivation
Day 2 (Monday, May 13th 2019) - Overview of Machine Learning (In-Person, 6.30 pm - 10 pm)
  • Supervised and unsupervised learning
  • Parametric and non-parametric models
  • Comparisons, metrics, and selection
Day 3 (Tuesday, May 14th, 2019) - Overview of Machine Learning (Virtual) 
  • Model Selection and Model Estimation
  • Application oriented assignments (in R) 
Day 4 (Wednesday, May 15th 2019) - Methods of Unsupervised Learning (In-Person, 6.30 pm - 10 pm)
  • Clustering models
    • Flat Models
    • Hierarchical Models
  • Graphical Models
    • Bayesian Networks, Hidden Markob Models, Exact and Approximate Infererence
    • Other Graphical Models
Day 5 (Thursday, May 16th, 2019) – Methods of Unsupervised Learning (Virtual)
  • Dimensionality Reduction and Bayesian Approaches ( R-exercises)
    • Factor Analysis vs. PCA
    • Latent Dirichlet Analysis (LDA)
    • Boltzmann Machines
Day 6 (Friday, May 17th, 2019) – Methods of Unsupervised Learning (Virtual)
  • Mixture Models for Unsupervised Learning (R-exercises)

Day 7 (Saturday, May 18th, 2019) - Methods of Supervised Learning (Virtual)
  • Deep Learning (R-exercises)
    • Generative vs. Discriminative Models
    • Regression vs. Classification
Day 8 (Sunday, May 19th, 2019) - Methods of Supervised Learning (Virtual)
  • Deep Learning (R-exercises)
    • Artificial Neural Networks
    • Deep Learning
Day 9 (Monday, May 20th, 2019) - Methods of Supervised Learning (Virtual) 
  • Naïve-Bayes Classifiers
  • Support Vector Machines
Day 10 (Tuesday, May 21st, 2019) - Bringing It All Together (In-Person, 6.30 pm - 10 pm)
  • Case Studies in Unsupervised and Supervised Learning
  • Evaluation /presentations and closing

Course Notes

• All topic material will be demonstrated in R and associated tools.
• All topic material will focus primarily on the implementation and application of machine learning methods, models and packages /libraries.
• The methods will be lectured more from a conceptual rather than technical perspective, primarily illustrated through case studies, example problems, and R scripts.
• All participants / students will be expected to have sufficient knowledge in R to be able to follow along with the workshops.
• Participants will be expected to complete their virtual readings and activities on time
• If any participant misses a session in whole or in part: It will entirely be the participants’ responsibility to make up for it in all possible ways. The workshop instructor or professor must not be expected to repeat material, provide extra help or make any concessions for missed sessions or other participant shortcomings such as unconfigured laptops /other issues.
• Topics and content may be changed at the discretion of the Workshop instructors depending on a variety of factors including participant ability and need. Participants will be updated on changes if any.
• Successful participants will be awarded with a GBFI certification of completion for the Machine Learning Workshop.


Please click here to register. Please note the registration deadline is Monday, May 6th, 2019

*This course is free for current, full-time WPU students. 
*Course offerings are subject to enrollment and may be cancelled at any time prior to the event.