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Course Outline
Introduction
Probability Theory, Model Selection, Decision and Information Theory
Probability Distributions
Linear Models for Regression and Classification
Neural Networks
Kernel Methods
Sparse Kernel Machines
Graphical Models
Mixture Models and EM
Approximate Inference
Sampling Methods
Continuous Latent Variables
Sequential Data
Combining Models
Summary and Conclusion
Requirements
- Understanding of statistics.
- Familiarity with multivariate calculus and basic linear algebra.
- Some experience with probabilities.
Audience
- Data analysts
- PhD students, researchers and practitioners
21 Hours
Testimonials (2)
It felt like we were going through directly relevant information at a good pace (i.e. no filler material)
Maggie Webb - Department of Jobs, Regions, and Precincts
Course - Introduction to the use of neural networks
Working from first principles in a focused way, and moving to applying case studies within the same day