First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Science
Course unit
SCP7079279, A.A. 2018/19

Information concerning the students who enrolled in A.Y. 2018/19

Information on the course unit
Degree course Second cycle degree in
SC1176, Degree course structure A.Y. 2014/15, A.Y. 2018/19
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Number of ECTS credits allocated 6.0
Type of assessment Mark
Course unit English denomination COGNITIVE SERVICES
Website of the academic structure
Department of reference Department of Mathematics
Mandatory attendance No
Language of instruction English
Single Course unit The Course unit can be attended under the option Single Course unit attendance
Optional Course unit The Course unit can be chosen as Optional Course unit

Teacher in charge LAMBERTO BALLAN INF/01

Course unit code Course unit name Teacher in charge Degree course code

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Core courses INF/01 Computer Science 6.0

Course unit organization
Period Second semester
Year 1st Year
Teaching method frontal

Type of hours Credits Teaching
Hours of
Individual study
Laboratory 2.0 16 34.0 No turn
Lecture 4.0 32 68.0 No turn

Start of activities 25/02/2019
End of activities 14/06/2019
Show course schedule 2019/20 Reg.2014 course timetable

Examination board
Board From To Members of the board
2 a.a. 2018/2019 01/10/2018 28/02/2020 BALLAN LAMBERTO (Presidente)
CONTI MAURO (Supplente)
1 a.a. 2017/2018 01/10/2017 28/02/2019 SPERDUTI ALESSANDRO (Presidente)
BALLAN LAMBERTO (Membro Effettivo)
BRESOLIN FABIO (Membro Effettivo)
CONTI MAURO (Membro Effettivo)
TOLOMEI GABRIELE (Membro Effettivo)

Prerequisites: The student should have basic knowledge of programming and algorithms. It is also advisable to be familiar with basic concepts in probability and analysis of multivariate functions.
Target skills and knowledge: This class teaches the concepts, methods, and technologies at the basis of Cognitive Services, i.e. APIs, SDKs and services, typically available in the cloud, that help software developers to create artificial intelligent applications. Examples of intelligent features that can be added to an application via cognitive services are: emotion and video detection; facial, vision and speech recognition; speech and language understanding.
The class also teaches the specific skills and abilities needed to apply those concepts to the design and implementation of artificial intelligent applications.
Students will face practical exercises in a computer lab that allow them to test the application of the acquired knowledge to small practical examples.
Examination methods: The student is expected to develop, in agreement with the teacher, a small applicative project. In addition, the student must submit a written report on the project, addressing in a critical fashion all the issues dealt with during its realization. The student will present and discuss the project and, if deemed necessary by the teacher, pass an oral examination.
Assessment criteria: The project work, and the eventual oral examination, will be evaluated on the basis of the following criteria: i) student’s knowledge of the concepts, methods, and technologies at the basis of Cognitive Services; ii) ability of the student to master the implementation technology; iii) student’s capacity for synthesis, clarity, and abstraction, as demonstrated by the written report and project presentation.
Course unit contents: The course will cover the topics listed below:
- Introduction:
From human cognition to smart cognitive services; brief intro to AI and ML paradigms.
- Cognitive Services:
Basic concepts; Language, Speech, and Vision Services; major services and API (IBM Watson, Microsoft, Google Cloud); enabling technologies.
- Machine Learning and Application Issues:
Classification; Representation learning and selection of categorical variables; Training and testing; Evaluation measures.
- Visual Recognition:
“Teaching computers to see”: extract rich information from visual data; Challenges: why is computer vision hard?; Designing effective visual features; Representation learning in computer vision; Image understanding.
- Hands-on Practicals:
What’s in the box? How to build a visual recognition pipeline; Using cognitive services for image recognition/understanding; Combining different services in a multi-modal scenario.
Planned learning activities and teaching methods: The course consists of lectures and exercises in the computer lab. The exercises in the computer lab allow the students to experiment, under various operating scenarios, with the techniques introduced in class. In this way, students can verify experimentally the concepts learned in class and acquire the ability to apply the learned concepts and to perform critical judgment.
Additional notes about suggested reading: Slides presented during the lectures are made ​​available to students as reference material.
Textbooks (and optional supplementary readings)