Course Materials

Introduction au Machine learning (French content):

Lecture 1: INTRODUCTION AU MACHINE LEARNING
Lecture 2: ALGORITHME DE DESCENTE DU GRADIENT
Lecture 3: REGRESSION LINEAIRE
Lecture 4: ALGORITHME K-MEANS (CLUSTERING)
Lecture 5: ALGORITHME KNN (CLASSIFICATION)
Lecture 6: COMPRENDRE LES METRIQUES D'EVALUATION
Lecture 7: INTRODUCTION AU DEEP LEARNING
Lecture 7 Bonus: DEEP LEARNING EN "20" QUESTIONS GÉNÉRALES

Data-driven project scheduling:

Lecture 1: Project scheduling in the era of DATA and IA
Lecture 2: Graham’s classification and basic heuristics
Lecture 3: Python lab – SPT, EDD, Hodgson and Moore
Lecture 4: Descriptive statistics for bottleneck detection (Excel)
Lecture 5: Inferential statistics for strategic decisions (Excel)
Lecture 6: Graph coloring from mathematical curiosity to project scheduling tool
Lecture 7: Machine learning for project scheduling (Introduction to Kmeans)
Lecture 8: Lab session – Deploying Kmeans with Python and Excel
Lecture 9: Machine learning for project scheduling (Introduction to KNN)
Lecture 10: ...
Lecture 11: Machine learning for project scheduling (Introduction to SVM)
Lecture 12: Introduction to Deep learning

Operations research:

Lecture 1: Introduction to operations research
Lecture 2: Mathematical modeling
Lecture 3: Graphical approach
Lecture 4: Convexity
Lecture 5: Intuitive approach to the simplex algorithm