| Course label : | Models for Machine Learning |
|---|---|
| Teaching departement : | EEA / Electrotechnics - Electronics - Control Systems |
| Teaching manager : | Mister PIERRE-ANTOINE THOUVENIN / Mister PIERRE CHAINAIS |
| Education language : | |
| Potential ects : | 0 |
| Results grid : | |
| Code and label (hp) : | MR_DS_S2_MML - Models for Machine Learning |
Education team
Teachers : Mister PIERRE-ANTOINE THOUVENIN / Mister PIERRE CHAINAIS
External contributors (business, research, secondary education): various temporary teachers
Summary
Fundamentals of decision theory and Bayesian models. Hands on. Usual methods and notions in this scope: - baseline concepts (likelihood, prior, posterior, ) - conjugate priors, uninformative priors, exponential family; - hierarchical models; - graph representation with directed acyclic graphs (DAGs); - expected utility and posterior expected loss; - Bayesian estimators; - exact inference with Markov chain Monte Carlo algorithm (MCMC) : the Gibbs sampler; - approximate Bayesian inference (variational Bayes, EM).
Educational goals
- identify a relevant model in light of the available data; - formalize the learning problem as an optimization problem; - formalize the learning problem as a decision under uncertainty problem; - understand the connections between deterministic and probabilistic modelings; - understand the implications of the chosen model on the results; - implement efficiently the concepts allowing to make optimal decisions.
Sustainable development goals
Knowledge control procedures
Continuous Assessment
Comments: Continuous evaluation.
Labs, grading scale: (min) 0 – 20 (max)
Project, grading scale: (min) 0 – 20 (max)
Online resources
"Machine learning, a probabilistic perspective", K. Murphy, 2012. "The Bayesian choice", Christian Robert, 2007. "Pattern recognition and Machine Learning", Christopher Bishop, 2006.
Pedagogy
Labs and tutorial sessions. Language of instruction is specified in the course offering information in the course and programme directory. English is the default language.
Sequencing / learning methods
| Number of hours - Lectures : | 12 |
|---|---|
| Number of hours - Tutorial : | 12 |
| Number of hours - Practical work : | 0 |
| Number of hours - Seminar : | 0 |
| Number of hours - Half-group seminar : | 0 |
| Number of student hours in TEA (Autonomous learning) : | 0 |
| Number of student hours in TNE (Non-supervised activities) : | 0 |
| Number of hours in CB (Fixed exams) : | 0 |
| Number of student hours in PER (Personal work) : | 0 |
| Number of hours - Projects : | 0 |
Prerequisites
Machine learning 1 or the equivalent. Probability 1 & 2. Statistics 1 & 2. Python and tools for research. Notions in optimization.