The main topics i teach are related to Data Analysis, Performance Evaluation, Optimisation and their Applications in Networks.

* Data Analysis for Networks (DataNets)
– Sorbonne University: 2019-2023
The material is available online [DataNets on GitHub] [Videos]
The course covers:
(L1) Probability
(L2) Frequentist Estimation
(L3) Hypothesis Tests (Confidence intervals, Neyman-Pearson)
(L4) Bayesian Inference (Bayes Rule, conjugate priors, MAP vs ML)
(L5-6) Regression (Linear, Polynomial),
(L7) Mode Selection and Cross-Validation,
(L8) Classification (KNN, Logistic, LDA),
(L8.b*) Feature Selection (Regularisation)
(L9) Tree-based models (Trees, Random Forests)
(L10) Clustering (K-means, Gaussian Mixture Model, Hierarchical)
(L11) PCA (and Anomaly Detection applications)

(L12) Deep Neural Networks
(L13-14) Time-Series
(L15) Support Vector Machines (classification, regression)

* Performance evaluation and queuing
– Telecom ParisTech RES711: 2015-2017
– Telecom ParisTech RES221: 2012-2015
– Master parisien en recherche opérationnelle (MPRO) INF947: 2014-2015

* Optimisation
– Telecom ParisTech MDI210: 2016-2019
The first (short) Part of the course covers basic concepts of Numerical Analysis (Matrices, Norms, Solution of systems of linear equations, Gauss method, Gauss-Jordan, LU-factorisation, Cholesky factorisation, Jacobi method to approximate the eigenvalues).
The second (largest) Part is on Optimisation (Linear Programming, Duality, Simplex Algorithm, Non-linear optimisation without constraints, Non-linear optimisation with constraints, Convex Programs, KKT conditions)

* Network access & Mobility
– Telecom ParisTech SOCOM207/TELECOM204: 2015-2020
Random Access ALOHA & CSMA [ALOHA Lab on GitHub]
– Telecom ParisTech RES222: 2013-2015
ARQ Protocols, Random Access.

* Telecommunication Networks
– Paris 6 University Pierre et Marie Curie RTEL: 2016-2017
Network Coding (Course and Excercises)

* Probability
– Telecom ParisTech MDI104: 2013-2016

* Stochastic Processes in Telecommunications
– TU Berlin: 2007-2009

* Stochastic Geometry
– Paris 6 University Pierre et Marie Curie CELL: 2016-2017
Point Processes in the Telecommunications (Course, [SlidesPP2016])
Another important topic for modelling wireless networks is Stochastic Geometry. Since it is quite hard to find something short but handy to get a start on the topic, I have prepared some slides. These were presented at the Summer School for PhD students, as part of the CROSSFIRE European Project in Barcelona, Spain, July 2014.

Stochastic Geometry Tutorial 2014: Part I, Part II.


(2019/2020): 69 hrs, 2018/2019: 34.5 hrs, (2017/2018): 34.5 hrs, (2016/2017): 71.25 hrs, (2015/2016): 30 hrs, (2014/2015): 36.75 hrs, (2013/2014): 12.75 hrs, (2012/2013): 6 hrs, (2008/2009): 4 hrs, (2007/2008): 4 hrs