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About the course

Review of Statistics with Resampling Techniques and Linear Algebra

From Regression to Support Vector Machines

Decision Trees, Ensemble Methods and Boosting

9. Decision trees, overarching aims
10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods

Dimensionality Reduction

Deep Learning Methods

Weekly material, notes and exercises

Exercises week 34
Week 34: Introduction to the course, Logistics and Practicalities
Exercises week 35
Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression
Exercises week 36
Week 36: Linear Regression and Statistical interpretations
Exercises week 37
Week 37: Statistical interpretations and Resampling Methods
Exercises week 38
Week 38: Logistic Regression and Optimization
Exercises week 39
Week 39: Optimization and Gradient Methods
Week 40: Gradient descent methods (continued) and start Neural networks
Exercises week 41
Week 41 Neural networks and constructing a neural network code
Exercises week 42
Week 42 Constructing a Neural Network code with examples
Exercises Week 42: Logistic Regression and Optimization, reminders from week 38 and week 40
Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
Exercises week 43
Week 44, Convolutional Neural Networks (CNN)
Week 45, Convolutional Neural Networks (CCNs) and Recurrent Neural Networks (RNNs)
Week 46: Decision Trees, Ensemble methods and Random Forests
Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods
Exercise week 47
Week 48: Gradient boosting and summary of course
Exercises week 48

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By Morten Hjorth-Jensen