## Teaching 2018/2019

### MDI210 Optimization et Analise númeric (Summer 2018)

Here are some good lecture notes on Linear Programming by Marco Chiarandini. Here are my lecture notes (WARNING: these notes are a work in progress!)1) numerical linear algebra

2) nonlinear optimization

3) My lecture slides on Linear Programming

### Master2 Optimization for Data Science (2018/2019)

Lecture slides:1) intro ML

2) convexity and smoothness

3) proximity operator, ISTA and FISTA.

Lecture notes on gradient descent proofs.

Exercises:

1) convexity and smoothness, (with answers)

3) GD and SGD on linear least squares, (with answers)

4) gradient descent and Ridge Regression, (with answers)

5) proximity operator, (with answers)

## Teaching 2015—2018

**Mathematics Revision for M.Sc. OR (Fall 2014, 2015).**Revision Slides

**Support Calculus Workshop (Spring 2013)**Exercises: Sequences and Series (solutions), Limits and quotient derivatives (solutions), Differentiation Review , Anti-derivatives (solutions), Substitution and Integration by parts (solutions), Partial Fractions and Trigonometric Substitution (solutions), Ordinary Differential Equations (solutions),

**Introduction to Machine Learning and Stochastic Gradient Methods:**part I, part II and an exercise. Part of the Spring School on Optimization and Data Science.

## TutorialsAdvanced Computing for Operational Research (Fall 2015)Calculus and its Applications (Spring 2013, 2014) Credit Scoring (Fall 2014) Fundamentals of Operational Research (Fall 2013, 2015) Fundamentals of Optimization (Fall 2012, 2015) Introduction to Linear Algebra (Fall 2012, 2013, 2014) Optimization Methods in Finance (Spring 2013, 2015) Proofs and Problem Solving (Spring 2013) |