# Teaching Activities at IMT Lille Douai

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