CS-500 / 6 credits

Teacher(s): Kaboli Amin, Roshan Zamir Amir

Language: English


Summary

The course focuses on the development of real-word AI/ML products. It is intended for students who have acquired a theoretical background in AI/ML and are interested in applying that toward developing AI/ML-oriented products.

Content

Keywords

Artificial Intelligence (AI), AI product managers, Innovation

Learning Prerequisites

Required courses

CS-233 Introduction to machine learning or CS-433 Machinie learning or equivalent course on the basics of machine learning and deep learning

Important concepts to start the course

  • Python programming
  • Bascis of deep learning and machine learning
  • Basics of probability and statistics

Learning Outcomes

By the end of the course, the student must be able to:

  • and understand opportunities for an AI product or using AI within an existing product
  • the development of AI features
  • Launch AI products successfully

Transversal skills

  • Demonstrate the capacity for critical thinking
  • Evaluate one's own performance in the team, receive and respond appropriately to feedback.
  • Communicate effectively, being understood, including across different languages and cultures.
  • Set objectives and design an action plan to reach those objectives.
  • Chair a meeting to achieve a particular agenda, maximising participation.
  • Resolve conflicts in ways that are productive for the task and the people concerned.
  • Make an oral presentation.
  • Take account of the social and human dimensions of the engineering profession.

Teaching methods

  • Formal lectures
  • Group activities
  • Class discussions
  • Simulation games
  • Hands-on exercises
  • Project-based learning
  • Real-world case studies
  • Guest lectures by leading academic and industry figuers

 

Expected student activities

  • Individual : Case evaluations, self-study, class discussions
  • In-group : In-class exercises, projects, simulations games
  • Presentation : Weekly presentations of assignments in coaching sessions

Assessment methods

Continuous evaluation of case reports, proejcts, individual and group presentations, class discussions, during the semester. More precisely :

25% Weekly in-class work and engagement

45% Class assignments, presentations, projects, and case reports

30% Final (final report and presentation and understanding of the case)

Supervision

Office hours Yes
Assistants Yes
Forum Yes

Resources

Bibliography

Ressources en bibliothèque

Moodle Link

In the programs

  • Semester: Fall
  • Exam form: During the semester (winter session)
  • Subject examined: AI product management
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Practical work: 3 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Fall
  • Exam form: During the semester (winter session)
  • Subject examined: AI product management
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Practical work: 3 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Fall
  • Exam form: During the semester (winter session)
  • Subject examined: AI product management
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Practical work: 3 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Fall
  • Exam form: During the semester (winter session)
  • Subject examined: AI product management
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Practical work: 3 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Fall
  • Exam form: During the semester (winter session)
  • Subject examined: AI product management
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Practical work: 3 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Fall
  • Exam form: During the semester (winter session)
  • Subject examined: AI product management
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Practical work: 3 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Fall
  • Exam form: During the semester (winter session)
  • Subject examined: AI product management
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Practical work: 3 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Fall
  • Exam form: During the semester (winter session)
  • Subject examined: AI product management
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Practical work: 3 Hour(s) per week x 14 weeks
  • Type: optional

Reference week

 MoTuWeThFr
8-9     
9-10     
10-11     
11-12     
12-13     
13-14     
14-15     
15-16     
16-17     
17-18     
18-19     
19-20     
20-21     
21-22     

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