 ## Teaching

### Teaching Activities at Universite Bretagne Sud (2018-present)

More details soon

### Teaching Activities at IMT Lille Douai (2009-2018)

Please find below a description of the lectures in which I am mostly involved.

##### DATA Science (M2) [Coordinator]
I have proposed and created in 2016 this course (120h approx.) - The topics covered here include:
• Statistical Methods for Machine Learning:
• Regression Problems
• Bayesian Methods
• Clustering and Mixture Models
• Neural Networks
• Classification
• Large Margin Classifiers Decision Tree
• Ensemble Methods
• Monte Carlo Methods
• Convex Optimization
• Times Series: hidden Markov model, Kalman filter and Particle Filtering
• Seminars from professional: Dataiku, Vekia, etc.
• Data Science Project (Challenge with real dataset on the “Kaggle inclass” platform)

##### Statistical Inference (L3) [Coordinator]
The objective is to introduce the following concepts:
• Hypothesis testing: definition, exhaustivity, Likelihood ratio, Neyman-Pearson, Bayes criterion,
• Parametric estimation: estimator, variance / bias, Fisher information, cramer-Rao bound, etc.

##### Signal Processing (L3) [Coordinator]
The objective is to introduce the following concepts:
• Deterministic signals:
• Classification of signals: finite energy, finite power, periodic
• Convolution and its properties; Delta functions and their properties.
• Fourier Analysis: Fourier Series and Transform
• Linear filtering: impulse response, stability, ideal filters (lowpass, highpass and band-pass)
• Digital signals: Filtering (structure, stability, causality), Z-transform and its inverse, discrete Fourier transform
• Random signals:
• Brief course reminders on Probability: random variables, distributions, common distributions.
• Ergodicity and stationarity
• Spectral analysis
• Filtering of stochastic process.

##### Probability (L3)
The objective of this course is to provide the basic principle for the analysis of random variables:
• Probability space
• Discrete and continuous random variables: cumulative distribution function, probability density function, expectation, change of variables
• Common probability density functions: uniform, Bernoulli, binomial, Poisson, normal, exponential,...
• Joint distributions, marginal and conditional distributions, covariance, correlation, independence