20734 - DATA INTELLIGENCE APPLICATIONS
Course taught in English
Go to class group/s: 31
Class-group lessons delivered on campus
The goal of the course is to provide advanced skills in machine learning and optimization for challenging real-world data-science applications. More precisely, the course explores mathematical models, optimization algorithms, and machine learning tools. In particular, the course focuses on 4 main real-world applications in which computational data science is crucial: pricing in e-commerce, digital advertising, social networks, matching. For every real-world application, the goals of the course are: providing a description of the real-world scenario and of the computational problems, providing mathematical models representing the scenario, providing optimization models, providing machine learning tools to deal with uncertainty.
1. Pricing in e-commerce 1.1. Introduction to pricing 1.2. Pricing a single product with infinite inventory 1.3. Pricing a single product with finite inventory 1.4. Laboratory 2. Digital advertising 2.1. Introduction to digital advertising 2.2. Pay-per-click optimization 2.3. Other issues 2.4. Laboratory 3. Social influence 3.1. Introduction to social influence 3.2. Population cascade models 3.3. Influence maximisation algorithms 3.4. Learning the network 3.5. Laboratory 4. Matching 4.1. Introduction to matching 4.2. Matching problems 4.3. Stochastic optimization for matching 4.4. Learning and matching 4.5. Laboratory |
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- Online lectures
- Exercises (exercises, database, software etc.)
- Group assignments
The course is based on a flipped-classroom approach. All the lectures are provided by means of videos available at the beginning of the course, while clarification lectures in the physical classrooms will be used to clarify potential doubts of the students. Some lectures will be devoted to exercises and laboratory activities. |
Continuous assessment | Partial exams | General exam | |
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The exam is composed as follows:
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The material (video lectures, questions, quizzes) is entirely provided by the lecturer.