30586 - MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE LAB
Department of Decision Sciences
Course taught in English
Go to class group/s: 95
Course Director:
RICCARDO ZECCHINA
RICCARDO ZECCHINA
Suggested background knowledge
For a fruitful and effective learning experience, it is recommended a preliminary elementary knowledge of algebra, calculus and probability.
Mission & Content Summary
MISSION
The purpose of this course is to present some basic methods of modern artificial intelligence, highlighting both their strength and their limitations.
CONTENT SUMMARY
- Elements of statistical inference
- Elements of machine learning
- Example of applications
- Simple programming exercises
Intended Learning Outcomes (ILO)
KNOWLEDGE AND UNDERSTANDING
At the end of the course student will be able to...
- undestand the main differences between the tools which compose modern AI
- understand the elementary aspects of the methodological foundation of machine learning
APPLYING KNOWLEDGE AND UNDERSTANDING
At the end of the course student will be able to...
- discriminate netween different problem types
- undestand tools should be used
- formulate questions in a quantitative form (optimization problem)
Teaching methods
- Online lectures
DETAILS
Each online lecture will be self-contained and devoted to a specific topic.
Assessment methods
| Continuous assessment | Partial exams | General exam | |
|---|---|---|---|
|
x |
ATTENDING AND NOT ATTENDING STUDENTS
Multiple choice question will serve the scope of checking that the students have acquired a sufficient level of understanding of the most basic and elementary methods and propblems of AI
Teaching materials
ATTENDING AND NOT ATTENDING STUDENTS
huandouts will be provided prior to the course
Last change 29/06/2021 17:21