#### Foundational Computational Biology I-II (2022)

Graduate course, Department of Molecular Genetics, University of Toronto. Note: only lectures given by me are listed below.

##### Lectures

- [1.1.2]
**An introduction to reproducible computational research**[slides]- Why work reproducibly?
- Pseudorandom number generation and seeds
- An introduction to Snakemake

- [1.3.2]
**An introduction to supervised machine learning**[slides]- An overview of supervised learning
- Linear regression models
- Loss optimization via gradient descent
- Classification with logistic regression
- More complex models
- Train/test splits
- Model complexity: overfitting and underfitting
- Penalized regression

- [2.1.1]
**Unsupervised learning: continuous latent variable models**[slides]- Historical perspective
- Principal component analysis and pPCA
- Non-negative matrix factorization
- Nonlinear methods: tSNE & autoencoders

- [2.1.2]
**Unsupervised learning: mixture models**[slides]- Clustering from a probabilistic perspective
- Gaussian Mixture Models
- Expectation-maximization
- Model selection

- [2.3.1]
**Introduction to Bayesian inference**[slides]- Re-introduction to Bayes rule
- Sampling methods: Gibbs sampling, Metropolis Hastings
- Variational inference
- An introduction to probabilistic programming languages & STAN

- [2.3.2]
**Introduction to deep learning**[slides]- The perceptron, multi-layer perceptrons
- Gradient descent and backpropagation
- Deep learning for images: CNNs
- Deep learning for sequence data: RNNs