Syllabus
Research Track - AQME CERTIFICATE - 12 ECTS -
- Machine Learning
Machine Learning
Ects : 6
Lecturer :
Total hours : 36
Overview :
The course gives a thorough presentation of the machine learning field and follows this outline:
- general introduction to machine learning and to its focus on predictive performances (running example: k-nearest neighbours algorithm)
- machine learning as automated program building from examples (running example: decision trees)
- machine learning as optimization:
- empirical risk minimization
- links with maximum likelihood estimation
- surrogate losses and extended machine learning settings
- regularisation and kernel methods (support vector machines)
- reliable estimation of performances:
- over fitting
- split samples
- resampling (leave-one-out, cross-validation and bootstrap)
- ROC curve, AUC and other advanced measures
- combining models:
- ensemble techniques
- bagging and random forests
- boosting
- unsupervised learning:
- clustering (hierarchical clustering, k-means and variants, mixture models, density clustering)
- outlier and anomaly detection
Coefficient : 2 6 (M2 Economie Internationale et Développement) 6 (M2 Diagnostic économique international)
Require prerequisites :
- intermediate level in either Python or R. Students are expected to be able to perform standard data management tasks in Python or R, including, but not limited to:
- loading a data set from a CSV file
- recoding and cleaning the data set
- implementing a simple data exploration strategy based on pivot table and on graphical representation
- intermediate level in statistics and probability. Students are expected to be familiar with:
- descriptive statistics
- conditional probabilities and conditional expectations
- core results from statistics: bias and variance concepts, strong law of large numbers, central limit theorem, etc.
Learning outcomes :
After attending the course the students will
- have a good understanding of the algorithmic and statistical foundations of the main machine learning techniques
- be able to select machine learning techniques adapted to a particular task (exploratory analysis with clustering methods, predictive analysis, etc.)
- be able to design a model selection procedure adapted to a particular task
- report the results of a machine learning project with valid estimation of the performances of their model
Assessment :
- quizzes and tests during the course
- machine learning project
CHOOSE ONE FIELD
THEORY FIELD
Mandatory courses - 9 ECTS
Elective specialization course -3 ECTS - choose one
- Environement and sustainability
Environement and sustainability
Ects : 3
Lecturer :
Total hours : 21
Overview :
Global warming and the related environmental and social issues raise serious concerns for the welfare of our current and future generations. Such changes require to develop new approaches and solutions to address these key issues so that they can become and remain sustainable. The course Environment and Sustainability will introduce students to key theories and models related to the environment, sustainability, societal issues, and the United Nations' Sustainable Development Goals.
1. Introduction: challenges for sustainability toward the net-zero economy
2. Sustainability: definition and examples
3.Sustainability: theoretical challenges
4.Climate Change: definition and examples
5.Climate Change: theoretical challenges
6.Climate Change policies
7.The energy transition
Coefficient : 2
Recommended prerequisites :
Advanced Micro and Macro Economics
Learning outcomes :
Students will be able to critically evaluate the complex drivers and consequences of global environmental problems for different societal groups, applying academic concepts and theories. They will develop in-depth knowledge in specialist areas of environment and sustainability and gain critical thinking skills. Finally, attendees will be able to assess the effectiveness, equity and trade-offs of different sustainability goals and policies.
Assessment :
Final Exam (written dissertation)
Bibliography-recommended reading
Dasgupta, Sir Partha. "The Economics of Biodiversity The Dasgupta Review Abridged Version." (2021).
Richard S. J. Tol, Climate Economics: Economic Analysis of Climate, Climate Change and Climate Policy Edward Elgar Publishing, 2019 - 234 pages
Selected Videos from rtol.github.io/ClimateEconomics/video/
Options : choose between the two blocks
Block 1 - Elective Quantitative courses - 6 ECTS
Block 2 - Elective Quantitative courses - 6 ECTS
SOCIAL AND PUBLIC POLICIES FIELD
Mandatory courses - 15 ECTS
Elective course - 3 ECTS - choose one
- Environement and sustainability
Environement and sustainability
Ects : 3
Lecturer :
Total hours : 21
Overview :
Global warming and the related environmental and social issues raise serious concerns for the welfare of our current and future generations. Such changes require to develop new approaches and solutions to address these key issues so that they can become and remain sustainable. The course Environment and Sustainability will introduce students to key theories and models related to the environment, sustainability, societal issues, and the United Nations' Sustainable Development Goals.
1. Introduction: challenges for sustainability toward the net-zero economy
2. Sustainability: definition and examples
3.Sustainability: theoretical challenges
4.Climate Change: definition and examples
5.Climate Change: theoretical challenges
6.Climate Change policies
7.The energy transition
Coefficient : 2
Recommended prerequisites :
Advanced Micro and Macro Economics
Learning outcomes :
Students will be able to critically evaluate the complex drivers and consequences of global environmental problems for different societal groups, applying academic concepts and theories. They will develop in-depth knowledge in specialist areas of environment and sustainability and gain critical thinking skills. Finally, attendees will be able to assess the effectiveness, equity and trade-offs of different sustainability goals and policies.
Assessment :
Final Exam (written dissertation)
Bibliography-recommended reading
Dasgupta, Sir Partha. "The Economics of Biodiversity The Dasgupta Review Abridged Version." (2021).
Richard S. J. Tol, Climate Economics: Economic Analysis of Climate, Climate Change and Climate Policy Edward Elgar Publishing, 2019 - 234 pages
Selected Videos from rtol.github.io/ClimateEconomics/video/
MACRO & FINANCE FIELD
Mandatory courses - 12 ECTS
Elective Quantitative courses - 6 ECTS - choose 2
- Asset pricing theory
Asset pricing theory
Ects : 3
Lecturer :
Total hours : 27
Overview :
In this course, we will discuss a wide range of topics ranging from optimal portfolio, the CAPM, factor models, consumption-based asset pricing, and arbitrage pricing, to more special ones including asymmetric information, and limits to arbitrage.
- Optimal Portfolio Theory and the CAPM
- Factor Models
- Decision Making under Uncertainty
- Consumption-based Asset Pricing
- Arbitrage Pricing
- Dynamic Asset Pricing
- Asymmetric Information and Asset Prices
- Limits to Arbitrage
Coefficient : 2
Learning outcomes :
Master the theoretical concepts of asset pricing
Assessment :
Evaluation: assignment 20%, final exam 80%
- Quantitative International Economics
Quantitative International Economics
Ects : 3
Lecturer :
Total hours : 21
Overview :
This lecture covers advanced topics in international economics with a special emphasis on quantitative techniques employed in international trade. This course is divided into two main components: the first part introduces important concepts and provides the theoretical foundations of the structural gravity equation. The second part deals with partial and general equilibrium trade policy analysis with structural gravity
Coefficient : 2
Recommended prerequisites :
Solid knowledge in Microeconomics, Econometrics and International Trade
Require prerequisites :
Microeconomics, Macroeconomics, Econometrics, International Trade
Learning outcomes :
- Enhance their understanding of economic methods and data sources for trade policy analysis.
- Applying international trade models and provides recommendations on how to obtain reliable partial and general equilibrium estimates for the effects of trade policy.
Assessment :
Home works
Learn more about the course :
Bibliography-recommended reading
- Head K. and T. Mayer, 2014. "Gravity Equations: Workhorse,Toolkit, and Cookbook", Handbook of International Economics, 4th ed, 4:131-195.
- Gravity Cookbook website
- Costinot, A., and A. Rodríguez-Clare, 2014. "Trade Theory with Numbers: Quantifying the Consequences of Globalization", Handbook of International Economics, 4th ed, 4:131-195.
- Yotov, Y. V., Piermartini, R., Monteiro, J. A., & Larch, M. (2016). An advanced guide to trade policy analysis: The structural gravity model. Geneva: World Trade Organization.
- Environement and sustainability
Environement and sustainability
Ects : 3
Lecturer :
Total hours : 21
Overview :
Global warming and the related environmental and social issues raise serious concerns for the welfare of our current and future generations. Such changes require to develop new approaches and solutions to address these key issues so that they can become and remain sustainable. The course Environment and Sustainability will introduce students to key theories and models related to the environment, sustainability, societal issues, and the United Nations' Sustainable Development Goals.
1. Introduction: challenges for sustainability toward the net-zero economy
2. Sustainability: definition and examples
3.Sustainability: theoretical challenges
4.Climate Change: definition and examples
5.Climate Change: theoretical challenges
6.Climate Change policies
7.The energy transition
Coefficient : 2
Recommended prerequisites :
Advanced Micro and Macro Economics
Learning outcomes :
Students will be able to critically evaluate the complex drivers and consequences of global environmental problems for different societal groups, applying academic concepts and theories. They will develop in-depth knowledge in specialist areas of environment and sustainability and gain critical thinking skills. Finally, attendees will be able to assess the effectiveness, equity and trade-offs of different sustainability goals and policies.
Assessment :
Final Exam (written dissertation)
Bibliography-recommended reading
Dasgupta, Sir Partha. "The Economics of Biodiversity The Dasgupta Review Abridged Version." (2021).
Richard S. J. Tol, Climate Economics: Economic Analysis of Climate, Climate Change and Climate Policy Edward Elgar Publishing, 2019 - 234 pages
Selected Videos from rtol.github.io/ClimateEconomics/video/
Data science course - Elective course - 3 ECTS - choose one
CHOOSE ONE FIELD
THEORY FIELD
Mandatory courses - 21 ECTS
Elective courses - 6 ECTS - choose 2
SOCIAL AND PUBLIC POLICIES FIELD
Mandatory courses - 27 ECTS
MACRO & FINANCE FIELD
Mandatory courses - 27 ECTS
Academic Training Year 2025 - 2026 - subject to modification
Teaching Modalities
All courses in the Quantitative Economic Analysis track are taught in English and delivered in lecture format. Since the program emphasizes research training, students will frequently read research articles, with the possibility of presenting some of them in front of the class.
The first semester runs from the end of August to December. The second semester spans from January to early March for the lectures. Courses in the second semester are followed by the completion of a Master thesis, which must be defended between May and November. Depending on their professional goals, students can choose between writing a PhD project or completing a mandatory internship.
Upon completion of each semester, students will receive 30 ECTS credits.
There are two mandatory core courses: Python for Data Science and Machine Learning. The selection of additional courses depends on the student’s chosen specialization, with three options available: Social and Public Policies, Economic Theory, and Macroeconomics and Finance. Depending on the selected field, students will take both quantitative and specialization courses at an advanced level, with a mix of mandatory and elective courses.
Most of the courses are concentrated in the first semester, while the second semester has a lighter teaching load. The majority of the second semester is devoted to writing the Master thesis and completing the internship or PhD project.
Internships and Supervised Projects
Each student in the Quantitative Economic Analysis track independently writes a research thesis, which accounts for 15 ECTS credits out of the 30 credits required in the second semester.
The Master thesis will be supervised by a researcher from the LEDa (Research Department in Economics at Dauphine-PSL) or a member of the Master’s pedagogical team.
The thesis gives rise to a written report and an oral defense, both of which are graded and contribute to the final assessment of the student.
Depending on their professional goals, students can choose between writing a PhD project or completing a mandatory internship, which can take place from March to the end of November.
The schedule is such that a student who chooses to work on a PhD project can also undertake an internship, although this will not be rewarded with ECTS credits.
Research-driven Programs
Training courses are developed in close collaboration with Dauphine's world-class research programs, which ensure high standards and innovation.
Research is organized around 6 disciplines all centered on the sciences of organizations and decision making.
Learn more about research at Dauphine