Course 3: Probabilistic Machine Learning
Probabilistic Machine Learning provides an in-depth discussion of machine learning/artificial intelligence models and their applications to quantitative finance.
In particular, Probabilistic Machine Learning covers the following topics:- Machine learning models for point and probabilistic prediction, including engineering and enhancements: feature bases, trees, neural networks, gradient boosting, lasso/ridge regularization, random forests, etc.
- Supervised learning models, including regression and classification: least-squares, generalized linear models (GLM), Perceptron, support vector machine (SVM), logistic, trees
- Unsupervised learning models, including autoencoders and graphical models: k-means clustering, Bayesian and Markov networks
- Financial applications of machine learning models for hedging, credit default and clustering
After the course you will obtain a Statement of Completion. Furthermore, you can choose to complete an optional code-based project that will count as one of the two Practical Projects towards the ARPM Certification.
Syllabus
The course covers in depth the Probabilistic Machine Learning topics in the Lab.Notation
Data Science overview
Machine learning foundations
Notable distributions
Supervised learning - regression
Supervised learning - classification
Unsupervised learning - autoencoders
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Unsupervised learning - graphical models
Estimation - probabilistic
Estimation - classification