Objectives of Programme's Learning Outcomes

• Imagine, design, develop, and implement conceptual models and computer solutions to address complex problems including decision-making, optimisation, management and production as part of a business innovation approach by integrating changing needs, contexts and issues (technical, economic, societal, ethical and environmental).
• On the basis of modelling, design a system or a strategy addressing the problem raised; evaluate them in light of various parameters of the specifications.
• Mobilise a structured set of scientific knowledge and skills and specialised techniques in order to carry out computer and management engineering missions, using their expertise and adaptability.
• Master and appropriately mobilise knowledge, models, methods and techniques specific to computer management engineering.
• Assess the validity of models and results in view of the state of science and characteristics of the problem.
• Plan, manage and lead projects in view of their objectives, resources and constraints, ensuring the quality of activities and deliverables.
• Define and align the project in view of its objectives, resources and constraints.
• Work effectively in teams, develop leadership, and make decisions in multidisciplinary, multicultural and international contexts.
• Interact effectively with others to carry out common projects in various contexts (multidisciplinary, multicultural, and international).
• Communicate and exchange information in a structured way - orally, graphically and in writing, in French and in one or more other languages - scientifically, culturally, technically and interpersonally, by adapting to the intended purpose and the relevant public.
• Argue to and persuade customers, teachers and boards, both orally and in writing.
• Contribute by researching the innovative solution of a problem in engineering sciences.
• Construct a theoretical or conceptual reference framework, formulate innovative solutions from the analysis of scientific literature, particularly in new or emerging disciplines.
• Develop and implement conceptual analysis, numerical modelling, software implementations, experimental studies and behavioural analysis.
• Adequately interpret results taking into account the reference framework within which the research was developed.
• Communicate, in writing and orally, on the approach and its results in highlighting both the scientific criteria of the research conducted and the theoretical and technical innovation potential, as well as possible non-technical issues.

Learning Outcomes of UE

This class serves as an introduction to first-order methods to tackle large-scale ooptimization problems that arise for example in data analysis and machine learning. A particular attention will be given to stochastic gradient methods that are used to compute the weights in deep neural networks. 

UE Content: description and pedagogical relevance

Organization of the calss:  - Introduction and motivation to the use of firt-order methods.  - Optimal first-order methods for convex optimization.  - Stochastic gradient methods.  - Project: Comparison of first-order methods for solving a classification problem. 

Prior Experience

Non-linear optimization