Biological data science I: statistical learning
Summary
Processing, analyzing, and interpreting large biological datasets is an essential skill for modern biologists. This course aims to provide the theoretical foundations, analytical techniques, and software tools necessary to effectively manage and derive insights from complex biological data.
Content
Biological data types
Probability Distributions in Biology data
Maximum Likelihood estimators for Univariate and Bivariate Distributions
Statistical tests
Multivariate data analysis
Multivariate Linear Regression
Principal Component Analysis (PCA)
Clustering
Priors, Bayes, and Maximum a Posteriori Estimation
Advanced Linear Regression
Logistic Regression and Classification
Model Selection
Resampling and Simulations
Time series and 1D Signal Processing
ND-image Processing
Generative Models and MCMC
Keywords
Biological data, statistical learning, probability distributions, maximum likelihood estimation, multivariate analysis, multivariate normal, PCA, SVD, multivariate regression, classification, Bayesian inference, time series, image processing, resampling methods, MCMC.
Learning Prerequisites
Required courses
Analysis, Linear Algebra, Probability and Statistics
Learning Outcomes
By the end of the course, the student must be able to:
- Analyze multidimensional biological data
- Apply regression and classification models
- Perform model selection
- Use PCA and interpret it
- Visualize multivariate data
- Explore different types of biological data
- Implement basic routines of ML and MAP estimation
- Choose the most appropriate model to specific situation
- Plan an analysis end-to-end
- Interpret statistical tests and posterior distributions
Teaching methods
Lectures and excercises
Assessment methods
Written examination at the exam session (70%) and graded exercises (30%).
Supervision
| Office hours | No |
| Assistants | Yes |
| Forum | Yes |
Resources
Virtual desktop infrastructure (VDI)
No
Bibliography
Main:
"Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman
"Methods of Multivariate Analysis" By Alvin C. Rencher
Other resources:
"Computer Age Statistical Inference" by Bradley Efron and Trever Hastie
"Data-Driven Science and Engineering" by Steven L. Brunton and J. Nathan Kutz
Ressources en bibliothèque
- "Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman
- Methods of Multivariate Analysis" By Alvin C. Rencher
- "Computer Age Statistical Inference" by Bradley Efron and Trever Hastie
- "Data-Driven Science and Engineering" by Steven L. Brunton and J. Nathan Kutz
Notes/Handbook
Course notes in pdf format
Moodle Link
In the programs
- Semester: Spring
- Exam form: Written (summer session)
- Subject examined: Biological data science I: statistical learning
- Courses: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: mandatory