Resources
Machine learning
- Introduction to neural networks [slides, notes]
- Yet another introduction to backpropagation
- Autoencoder, variational AE, vector-quantised VAE and categorical VAE
- Yet another introduction to probabilistic graphical models
- Gibbs sampling for fitting finite and infinite Gaussian mixture models [code]
- Vector and matrix calculus
- Dynamic programming
- Principal components analysis
- Introduction to machine learning (DatA414)
- Introduction to natural language processing (NLP817)
Speech and signal processing
- Speech 101: Introduction to speech processing
- Dynamic time warping [slides, notebook]
- Introduction to speech features [slides]
- Introduction to digital signal processing (SS414)
About research
- Notes on writing
- Our Git workflow [notes]
- Choose your own adventure: Some thoughts on research
- Figuring out what is worth figuring out
About teaching
- How I make lecture videos
- Why I should make lecture videos (even if there are better ones out there)
- My teaching philosophy