1. Describe and apply basic probabilistic graphical models such as markov random fields and Bayesian networks
2. Explain hidden markov models
3. Apply Bayes and causal inference to solving tasks
4. Apply basic probabilistic graphical models to solving common biological problems
5. Identify appropriate graphical probabilistic models that can be applied to a given domain

Lectures in the classroom

Project team work.

1. Graphical models for computational biology
2. Graphical models for computational biology
3. Graphical models for computational biology
4. Graphical models for computational biology
5. Graphical models for computational biology
6. Hidden Markov models for computational biology
7. Hidden Markov models for computational biology
8. Midterm exam
9. Hidden Markov models for computational biology
10. Bayesian inference for computational biology
11. Bayesian inference for computational biology
12. Bayesian inference for computational biology
13. Application to complex biological systems
14. Application to complex biological systems
15. Final exam