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Course 2020-2021 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


Lezioni della classe erogate online

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

    Full credit is awarded to students who actively participate in all the activities organized by the host company.

    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 30/07/2020 12:36