Info
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Course 2020-2021 a.y.

20734 - DATA INTELLIGENCE APPLICATIONS

CYBER
Cross-institutional study L. Bocconi - Politecnico Milano

Course taught in English


Go to class group/s: 31

CYBER (6 credits - II sem. - OP  |  ING-INF/05)
Course Director:
NICOLA GATTI

Classes: 31 (II sem.)
Instructors:
Class 31: NICOLA GATTI


Mission & Content Summary
MISSION

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.

CONTENT SUMMARY

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


Intended Learning Outcomes (ILO)
KNOWLEDGE AND UNDERSTANDING
At the end of the course student will be able to...
  • know what is online machine learning,
  • know what is combinatorial optimization in economics,
  • know what are the challenges in applying learning in concrete online scenarios.
APPLYING KNOWLEDGE AND UNDERSTANDING
At the end of the course student will be able to...
  • model real-world applications by means of mathematical models from microeconomics,
  • identify the most suitable class of algorithms to solve a given problem,
  • use the tools already available to solve a given problem in practice,
  • design an algorithm to solve a given problem whenever no algorithm is known
  • how to design an algorithm to solve a given problem whenever no algorithm is known.

Teaching methods
  • Online lectures
  • Exercises (exercises, database, software etc.)
  • Group assignments
DETAILS

 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.


Assessment methods
  Continuous assessment Partial exams General exam
  • Written individual exam (traditional/online)
  •   x x
  • Group assignment (report, exercise, presentation, project work etc.)
  •   x  
    ATTENDING AND NOT ATTENDING STUDENTS

    Teaching materials
    ATTENDING AND NOT ATTENDING STUDENTS

    The material (video lectures, questions, quizzes) is entirely provided by the lecturer.

    Last change 15/07/2020 20:38