teaching |

I teach courses spanning pure mathematics, applied mathematics, statistics, and data science / machine learning, at both undergraduate and graduate levels. Below is an overview of the 38 courses with full lecture notes.

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Full bilingual (FR+EN) materials for all 38 courses are available in the course catalogue.

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Six courses also have a full cohort site — weekly schedule, readings, problem sets, code labs, paper discussions. The cohort layer for participants enrolled in a live cohort, or for self-study readers who want more than the PDF.

TDA Geometric Deep Learning Reinforcement Learning MLOps Generative AI ML Foundations

Teaching philosophy

Mathematics is best learned by doing. My teaching reflects that: rigorous theory paired with hands-on projects, where students prove a fixed-point theorem one week and build a TDA pipeline in Python the next. Theory and practice end up checking each other.

I emphasize active learning — problem sessions, coding labs, collaborative projects — over passive lectures. The other half of the job is mentorship: helping students figure out what research question is theirs, building the mathematical maturity to answer it, and making the path from student to contributor as concrete as I can.

Institutions

IMSP — Institut de Mathématiques et de Sciences Physiques, Dangbo, Benin
AIMS South Africa — African Institute for Mathematical Sciences, Cape Town
AIMS Senegal — African Institute for Mathematical Sciences, Mbour
AIMS Rwanda — African Institute for Mathematical Sciences, Kigali

Courses at AIMS — African Institute for Mathematical Sciences

Selected courses taught at AIMS centres (Rwanda, Senegal, South Africa). Each course has a dedicated interactive Jupyter Book with lecture notes, exercises, and code.

Python programming for scientists

An introduction to Python for scientific computing and data science. Variables, data structures, flow control, functions, NumPy, Matplotlib. Hands-on labs and programming challenges.

AIMS South Africa AIMS Senegal AIMS Rwanda

Jupyter Book

Experimental mathematics with SageMath

Computational problem-solving through experimentation. Discrete mathematics, number theory, linear algebra, graph theory, combinatorics. Building SageMath notebooks for mathematical exploration.

AIMS South Africa

Course site

Ordinary differential equations

Existence and uniqueness theorems, first and second order equations, systems of ODEs, stability analysis, Laplace transforms. Applications to population dynamics and epidemiological models.

AIMS Senegal AIMS Rwanda

Course materials

Topological data analysis

Persistent homology, simplicial complexes, Vietoris-Rips filtrations, Mapper algorithm, stability theorems. Implementation with GUDHI and Ripser in Python. Applications to health and financial data.

AIMS South Africa AIMS Senegal

Course materials

Numerical methods with Python

Root finding, interpolation, numerical integration, linear systems, ODE solvers. Error analysis and convergence. All methods implemented from scratch and with NumPy/SciPy, with Jupyter notebooks.

AIMS Senegal AIMS Rwanda

Course materials

Recommended learning paths

Pure Mathematics track: Linear Algebra → Real Analysis → General Topology → Algebraic Topology → Fixed Point Theory → TDA

Applied Mathematics track: Probability → Statistics → Stochastic Processes → Time Series → Bayesian Statistics → Quantitative Finance

Data Science & ML track: Programming → Intro to Data Science → Machine Learning → Deep Learning → NLP / Geometric DL / Reinforcement Learning → MLOps

Each arrow represents a suggested prerequisite. Students can enter at any point matching their background.

Pure mathematics

Undergraduate

General Topology — Open/closed sets, continuity, compactness, connectedness, product & quotient spaces
PDF FR
Real Analysis I & II — Sequences, series, limits, continuity, differentiation, Riemann integration, metric spaces
PDF FR (I) PDF FR (II)
Abstract Algebra I & II — Groups, rings, fields, homomorphisms, quotient structures, Galois theory
PDF FR (I) PDF FR (II)
Linear Algebra — Vector spaces, linear maps, eigenvalues, inner product spaces, canonical forms
PDF FR
Complex Analysis — Analytic functions, Cauchy's theorem, residues, conformal mappings
PDF FR
Differential Equations (ODE) — First & second order equations, systems, Laplace transforms, stability
PDF FR
Number Theory — Divisibility, congruences, primes, quadratic reciprocity, arithmetic functions
PDF FR
Discrete Mathematics — Combinatorics, graph theory, logic, proof techniques
PDF FR

Graduate

Algebraic Topology — Fundamental group, covering spaces, singular homology, cohomology, exact sequences
PDF FR
Differential Topology — Smooth manifolds, tangent bundles, transversality, Morse theory
PDF FR
Point-Set Topology (Advanced) — Quasi-metric spaces, asymmetric topology, T0-spaces, bitopological spaces
PDF FR
Fixed Point Theory — Banach contraction principle, Brouwer & Schauder theorems, generalized metric spaces
PDF FR
Functional Analysis — Banach & Hilbert spaces, bounded operators, spectral theory, Hahn-Banach theorem
PDF FR
Measure Theory & Integration — sigma-algebras, Lebesgue measure, Lp spaces, Radon-Nikodym theorem
PDF FR
Riemannian Geometry — Connections, curvature, geodesics, comparison theorems
PDF FR
Category Theory — Functors, natural transformations, limits, adjunctions, Yoneda lemma
PDF FR

Applied mathematics & statistics

Undergraduate

Probability Theory — Sample spaces, random variables, distributions, expectation, law of large numbers
PDF FR
Mathematical Statistics — Estimation, hypothesis testing, confidence intervals, regression
PDF FR
Numerical Analysis — Root finding, interpolation, numerical integration, error analysis
PDF FR
Partial Differential Equations — Heat, wave & Laplace equations, separation of variables, Fourier series
PDF FR
Operations Research — Linear programming, optimization, simplex method, duality, network flows
PDF FR
Mathematical Modelling — Formulation, dimensional analysis, dynamical systems, epidemiological models
PDF FR

Graduate

Stochastic Processes — Markov chains, Poisson processes, Brownian motion, martingales
PDF FR
Convex Optimization — Convex sets & functions, duality, gradient descent, interior-point methods
PDF FR
Dynamical Systems & Chaos — Stability, bifurcation, Lyapunov exponents, strange attractors
PDF FR
Quantitative Finance — Black-Scholes, stochastic calculus, portfolio optimization, risk measures
PDF FR
Time Series Analysis — ARIMA, GARCH, spectral analysis, state-space models, forecasting
PDF FR
Bayesian Statistics — Prior/posterior, MCMC, hierarchical models, Bayesian inference
PDF FR

Data science & machine learning

Undergraduate / introductory

Introduction to Data Science — Data wrangling, visualization, exploratory analysis (Python/R)
PDF FR
Machine Learning Foundations — Supervised & unsupervised learning, model evaluation, bias-variance
PDF FR
Programming for Scientists — Python, R, NumPy, Pandas, Matplotlib, scientific computing
PDF FR
Database Systems & SQL — Relational databases, queries, normalization, data pipelines
PDF FR
Data pre-processing — Missing data, outliers, encoding, feature engineering, text preprocessing, pipelines
PDF FR PDF EN
Julia programming — Types, multiple dispatch, DataFrames, visualization, performance, scientific computing, ML
PDF EN

Graduate / advanced

Topological Data Analysis (TDA) — Persistent homology, simplicial complexes, Mapper, stability theorems
PDF FR
Geometric Deep Learning — Graph neural networks, manifold learning, equivariant architectures
PDF FR
Deep Reinforcement Learning — MDPs, policy gradients, DQN, actor-critic, multi-agent RL
PDF FR
Deep Learning — CNNs, RNNs, transformers, attention, generative models (GANs, VAEs, diffusion)
PDF FR
Natural Language Processing — Embeddings, sequence models, LLMs, fine-tuning, RAG
PDF FR
MLOps & Reproducible Research — Experiment tracking, model deployment, Docker, CI/CD for ML
PDF FR
Generative AI — Transformers, GPT, prompt engineering, fine-tuning (LoRA), RAG, diffusion models, agents
PDF FR PDF EN
Data analysis for health specialists — Pandas, epidemiological measures, hypothesis testing, regression, ML for clinical prediction, geospatial health data
PDF EN PDF FR

Workshops & short courses (3-5 days)

Existing Workshops

Workshop on Computational Topology & Quantum Computing (WoComToQC) — Organizer & lecturer
Data Science Africa — Machine learning tutorials for African researchers
Python for Mathematical Research — Hands-on computing for mathematicians
Introduction to TDA with GUDHI & Ripser — Persistent homology in practice
The Shape of Data — Book-based workshop on geometry-driven ML and data analysis in R

Applied AI & industry

Introduction to generative AI & LLMs

3 days — Prompt engineering, fine-tuning, Retrieval-Augmented Generation (RAG), deployment. Hands-on with OpenAI API and open-source models.

Materials Notebooks

Data science for decision-makers

3 days — Non-technical training for managers and executives: understanding AI, identifying use cases, steering data projects, evaluating ROI.

Materials

MLOps in practice

4 days — From notebook to production: Docker, CI/CD pipelines, model monitoring, experiment tracking (MLflow), versioning (DVC).

Materials Notebooks

Mathematics & research

Reinforcement learning: from theory to practice

5 days — MDPs, Q-learning, DQN, policy gradients, actor-critic methods. Applications in resource allocation, game playing, and optimization.

Materials Notebooks

Geometric Deep Learning

4 days — Graph neural networks, learning on manifolds, equivariant architectures. Applications in molecular science, social networks, and point clouds.

Syllabus Notebooks

Applied Bayesian statistics

4 days — Bayesian modelling, MCMC, Stan/PyMC, hierarchical models. Applications in health, finance, and social science.

Syllabus Notebooks

Foundational skills

Python for data science

5 days — From zero to analysis: Pandas, data visualization, cleaning, exploratory analysis, and first ML models with scikit-learn.

Syllabus Notebooks

R for statistical analysis

4 days — Tidyverse, ggplot2, statistical modelling, reproducible reports with R Markdown. Companion to The Shape of Data.

Syllabus Notebooks

Scientific writing with LaTeX, Overleaf & Prism

3 days — Writing articles, theses, and dissertations with LaTeX. Collaborative editing on Overleaf and AI-assisted scientific writing with OpenAI Prism.

Syllabus Templates

Intensive bootcamps (8–12 weeks)

Cohort programs run through AIRINA Labs for working professionals and graduate students who want an intensive path to deployable ML systems, rather than a semester-long course. Open to corporate cohorts (banks, telecoms, insurance, public sector) and individual enrolment.

Machine Learning & AI Bootcamp — An experiential approach

10 weeks full-time or 20 weeks part-time. Ten modules from Python through classical ML, deep learning, NLP, LLMs and generative AI, MLOps — closing with a capstone project on a real dataset and a deployable portfolio piece.

60+ live sessions 1 capstone project 8 industry speakers 200 course hours 20 self-study hrs/wk (FT) · 10 hrs/wk (PT)

Modules: Python · Introduction to ML · Classical ML (classification, regression, clustering) · Recommender systems · NLP · ANN/CNN/RNN · LLMs & generative AI · MLOps & deployment · Capstone project · Portfolio

For: working professionals moving into ML/AI roles, upper-undergraduate and masters students looking for an intensive route, and corporate teams running an internal upskilling cohort.

Format: 100% online, synchronous. Live cohort sessions, hands-on Python labs, weekly office hours, capstone reviewed by working ML practitioners.

Syllabus Inquire about a cohort