Coding Project

Teacher(s)

Catanzaro Daniele;

Language

English

Prerequisites

The prerequisite(s) for this Teaching Unit (Unité d’enseignement – UE) for the programmes/courses that offer this Teaching Unit are specified at the end of this sheet.

Main themes

This course provides an introduction to mathematical modeling of computational problems. It covers the common algorithms, algorithmic paradigms, and data structures used to solve these problems. The course emphasizes the relationship between algorithms and programming. It pays particular attention on the practical importance of specific classes of optimization problems in management science and motivate the students to develop algorithms to solve them.   

Learning outcomes

At the end of this learning unit, the student is able to :
1	This course contributes to develop the following competencies. Knowledge Scientific reasoning and systematic approach Project management Leadership At the end of this course, students will: Improve their strategical thinking skills Acquire fundamental knowledge on the modeling and the resolution of practical problems Apply the appropriate techniques to propose a useful solution.

Content

This course introduces to algorithmic problem solving. Its main goal is to learn how to model practical problems arising from management engineering by using the most appropriate and efficient data structures, as well as how to implement the most efficient solution approaches by using classical algorithmic and graph theory. The course emphasizes the importance of both digitalization and the relationship between algorithms and programming, as well as the aspects related to project management and problem solving skills by means of the development of a final coding project aimed at solving a specific problem assigned each year. The problem may arise potentially by any area of management engineering or computer science; it may enjoy potentially any routing, partitioning, coloring, location, telecommunication, sustainable logistics and supply chain management, portfolio, scheduling, data mining or business analytics features, and may have any general structure. The students will have to work in group to tackle and solve it in the most efficient way as well as to be ready to defend their work during the examination session. 
The course includes in particular the following topics:

1. Algorithms and Algorithmic Analysis
2. Induction, Recursion, and Search
3. Fundation of data structures: Trees and Graphes
4. Basic algorithms on graphs
5. Brute-force search
6. Introduction to complexity classes
7. Well Solved Optimization Problems in Management Science - Part I: Spanning Trees
8. Well Solved Optimization Problems in Management Science - Part II: Shortest Paths
9. Hard Optimization Problems in Management Science - Part I - Spanning Trees with constraints 
10. Hard Optimization Problems in Management Science - Part I - Shortest Paths with constraints 

The participants to this course are strongly encouraged to pass the previous LSM bachelor courses in computer science before approaching this one and are assumed to be familiar with basic Python libraries such as Pandas and NumPy. 

Teaching methods

Standard blackboard lectures. Attending the course is strongly adviced and mandatory for the very first lecture.

Evaluation methods

The evaluation of this course is "unique" (art. 78 RGEE). It involves a group project of value max 40%; a single project of value max 30% and quiz in itinere of max 30%.This repartition must be intended as indicative only and may be adjusted from year to year in function of the projects addressed. The specific repartition is always communicated by the lecturer during the first mandatory lecture.

Other information

The main language of this course is English. 

Online resources

Please, refer to the slides of the course as well as to the official channel in Microsoft Teams.

Bibliography

Please, refer to the slides of the course. 

Teaching materials

• Please refer to the Teams platform

Faculty or entity

CLSM