This webinar is part of the Introduction to Data Science Webinar Series

Introductory session

• Who uses unsupervised learning?
• K-means
• Expectation-maximization
• Susceptibility to outliers
• Dangers of labeling clusters 

Watch recorded presentation below.

This webinar is part of the Introduction to Data Science Webinar Series

Practicum session

• Decision trees
• Problems in overfit
• Random Forest
• Out-of-bag error vs cross-validation

Watch recorded presentation below.

This webinar is part of the Introduction to Data Science Webinar Series

Introductory session

• Decision trees
• Problems in overfit
• Random Forest
• Out-of-bag error vs cross-validation

Watch recorded presentation below.

This webinar is part of the Introduction to Data Science Webinar Series

Practicum session

• Regression with many correlated variables
• Automatic variable selection, early approaches and problems
• Gradient descent
• Regularization (L1 vs L2 vs ElasticNet)

Watch recorded presentation below.

This webinar is part of the Introduction to Data Science Webinar Series

Introductory session

• Regression with many correlated variables
• Automatic variable seletion, early approaches and problems
• Gradient descent
• Regularization (L1 vs L2 vs ElasticNet)

Watch recorded presentation below.
 

This webinar is part of the Introduction to Data Science Webinar Series

Practicum session

• What is machine learning?
• Supervised vs unsupervised learning
• Model- and kernel-based methods
• Measures of accuracy (test/train and cross-validation)
• Causality and accuracy
• Unsupervised learning as feature reduction

Watch recorded presentation b

This webinar is part of the Introduction to Data Science Webinar Series

Introductory session

• What is machine learning?
• Supervised vs unsupervised learning
• Model- and kernel-based methods
• Measures of accuracy (test/train and cross-validation)
• Causality and accuracy
• Unsupervised learning as feature reduction

Watch recorded presentatio

This webinar is part of the Power of Population Data Science Series

Canada’s federal structure creates complexities for pan-Canadian health research. Although the healthcare system is funded at both the provincial and federal government level, responsibility for managing healthcare falls to provinces and territories (PTs). In the areas of mental illness and substance use more specifically, capacity for data collection and reporting varies across PTs.

This webinar is part of the Power of Population Data Science Series

Visualising results of statistical modeling is a key component of data science workflow. Statistical graphs are often the best means to explain and promote research findings.  However, in order to find that one graph that tells the story worth sharing, we sometimes have to try out and sift through many data visualizations. How should we approach such a task?