Facebook pixel
Info
Foto sezione
Logo Bocconi

Course 2021-2022 a.y.

30415 - TECHNOLOGICAL INNOVATION SEMINARS I

BEMACS
Department of Decision Sciences

Course taught in English


Go to class group/s: 25

BEMACS (1 credits - I sem. - OB)
Course Director:
EMANUELE BORGONOVO

Classes: 25 (I sem.)
Instructors:
Class 25: EMANUELE BORGONOVO


Mission & Content Summary
MISSION

Learn first-hand about the most recent developments in data science and applied Maths from renowned professional experts in the field.

CONTENT SUMMARY

Students will partecipate in a serie on Quantum Computing offered in Cooperation with IBM. The date is still do be determined. It will be a Friday in november to be agreed with IBM experts.


Intended Learning Outcomes (ILO)
KNOWLEDGE AND UNDERSTANDING
At the end of the course student will be able to...
  • Describe the data science problems illustrated in the seminar.
  • Recognize a number of "hot" topics in data science.
  • Identify the tools needed to solve these problems.
APPLYING KNOWLEDGE AND UNDERSTANDING
At the end of the course student will be able to...
  • Analyze the data science problems discussed in the seminars
  • Compare the different types of tools used to solve the different problems

Teaching methods
  • Online lectures
DETAILS

The students will be exposed to a high level presentation on Quantum Computing by international experts in the field.


Assessment methods
  Continuous assessment Partial exams General exam
  • Written individual exam (traditional/online)
  • x    
  • Active class participation (virtual, attendance)
  • x   x
    ATTENDING STUDENTS

    The exam aims to test the student understanding of the data science problem, the tools used to solved it and the methodological implications arising from the choice of such tool.. The assessment will consist of two parts: assessment of active class participation and a final online quiz to be taken after the seminar. The online quiz is aimed at testing the students' understanding of the data science problems, the "hot" topics in data science and the tools needed to solve these problems illustrated in the seminar.

    NOT ATTENDING STUDENTS

    A written exam is prepared for not attending students. Relevant topics are taken from the textbook.


    Teaching materials
    ATTENDING STUDENTS

    Materials are provided online through the learning platform some days before the visit.

    NOT ATTENDING STUDENTS
    • R. ROJAS, Neural Networks, A Systematic Introduction, Springer-Verlag Berlin Heidelberg, 1996.
    Last change 22/08/2021 21:50