Data-Driven Decision Making
Instance: 2020/2021 - 2S
Cycles of Study/Courses
Teaching Staff - Responsibilities
Teaching - Hours
|Theoretical and practical :
1. Get acquainted with the main supervised and unsupervised machine learning methods for analytics and decision support.
2. Learn how to formalize optimization models for prescriptive analytics using mathematical programming.
3. Get acquainted with languages and libraries for solving these problems.
4. Be able to critically analyze solutions obtained.
Learning outcomes and competences
What you'll learn:
1. An applied understanding of many different analytics methods, including linear regression, logistic regression, CART, clustering, and data visualization; focus on interpretable methods.
2. An applied understanding of mathematical optimization and how to solve these models with general-purpose solvers.
3. How to implement all of these methods.
1. Introduction: examples of usage on artificial intelligence and optimization.
2. Data analysis: case studies in Python.
3. Regression problems: background and case studies.
4. Classification problems: background and case studies.
5. Decision trees: definition and use. Random Forests.
6. Text analysis: use in knowledge acquisition.
7. Clustering and unsupervised learning methods.
8. Linear optimization: formulation and computational resolution.
9. Integer optimization: formulation and computational resolution.
10. Nonlinear optimization: formulation and computational resolution.
11. Final Considerations. Impact of the subjects taught on organizations.
Dimitris Bertsimas; The analytics edge
. ISBN: 978-0-9899108-9-7
Géron, A; Hands-on Machine Learning with Scikit-Learn and TensorFlow, O'Reilly Media, 2019. ISBN: 1492032646
Pedregosa, F. et al.; scikit-learn: Machine Learning in Python, 2019 (https://scikit-learn.org)
Bertsimas, D. and Freund, R.; Data, Models, and Decisions, Dynamic Ideas LLC, 2004
Teaching methods and learning activities
- Lectures: presentation of the program topics and discussion of examples.
- Labs: problem solving and case studies.
- Assignments: development and presentation of practical projects by students.
Physical sciences > Mathematics > Applied mathematics > Operations research
Physical sciences > Computer science > Cybernetics > Artificial intelligence
Distributed evaluation without final exam
|Trabalho prático ou de projeto
Amount of time allocated to each course unit
|Elaboração de projeto
|Elaboração de relatório/dissertação/tese
|Frequência das aulas
Eligibility for exams
Attendance to classes is mandatory.
Submission of proposed works.
Calculation formula of final grade
In-class questions (Quizzes): 20%
Practical assignments: 80%
There will be practical projects to be done in a group.
Special assessment (TE, DA, ...)
The same evaluation criteria is used for all students.
Jury: João Pedro Pedroso, Inês Dutra.