4A: Introduction to AI and Machine Learning
3 hrsSession lead: Dr Daniel Chalk
This session does not making use of a coding environment
Companion eBook
We also have a free eBook that gathers all of the coding notebooks from this module! Check it out at machinelearning.hsma.co.uk.
If you’d rather follow along with the recorded HSMA sessions and exercises, these can be found below.
All modules
10 sessions · 30 hours
-
-
4B: Logistic Regression - Who Would Survive the Titanic?
3 hrsSession lead: Dr Daniel Chalk
This session makes use of the base anaconda environment or the ml_sammi environment
-
4C: Ethics in AI
3 hrsSession lead: Dr Daniel Chalk
This session does not making use of a coding environment
-
4D: Decision Trees and Random Forests
3 hrsSession lead: Sammi Rosser
This session makes use of the ml_sammi environment
-
4E: Boosted Trees for Classification, and Tackling Regression (Numeric Prediction) Problems with Machine Learning
3 hrsSession lead: Sammi Rosser
This session makes use of the ml_sammi environment
-
4F: Neural Networks
3 hrsSession lead: Dr Daniel Chalk
This session makes use of the tf_hsma environment
-
4G: Explainable AI
3 hrsSession lead: Sammi Rosser
This session makes use of the ml_sammi environment
Slides Video - Introduction Video - Feature Importance in Logistic Regression & Odds/Log Odds/Probability Video - Feature Importance with MDI + PFI Video - Partial Dependence Plots and Individual Conditional Expectation Plots Video - Explainable AI with SHAP Video - Prediction Uncertainty Repository Book -
4H: Reinforcement Learning
3 hrs -
4I: Synthetic Data using SMOTE
3 hrsSession lead: Dr Daniel Chalk
This session makes use of the smote_hsma environment
-
4J: Optimising Machine Learning Models
3 hrsSession lead: Sammi Rosser
This session makes use of the complete_ml environment
No matching items