Intelligent System Project (ISP)

Master in Artificial Intelligence (UPC-URV-UB)

Fall semester, Course 2021/2022

(3 ECTS, 27 hours)

Responsible Professor:


Miquel Sŕnchez i Marrč
Intelligent Data Science and Artificial Intelligence Research Centre (IDEAI-UPC)
Knowledge Engineering & Machine Learning Group (KEMLG)
Dept. of Computer Science (CS)
Universitat Politčcnica de Catalunya · BarcelonaTech
Campus Nord-Building OMEGA, 2nd floor, office 203


Professor  Office
E-mail  Phone
Miquel Sŕnchez i Marrč OMEGA building,
  2nd floor, office 203
Campus Nord 93 413 78 41
To convene by e-mail


Day Hour Room
16-18 BUILDING A6, A6103
M. Sŕnchez-Marrč

Course schedule:

ISP lecturing: from September 16th, 2021 to December 9th, 2021

Holidays: November 4th, 2021 (mid-term exams period), since December 23th, 2021 until January 6th, 2022 (Christmas holidays)

        Change of schedule: none

1.  Introduction: Description of the aims of the course. Description of the team works. Information about the IS project timeline. Deliverables of the IS project. Examples of past ISP projects

2. Problem Analysis: Problem Feature Analysis. Information/Data Analysis. Viability Analysis. Economic Analysis. Environmental and Sustainability Analysis.

3. Definition of the Intelligent System project issues: Definition of main goals of the IS project. Definition of sub-goals. Task Analysis.

4. Development of an Intelligent System Project: Data/Information Extraction. Data Mining & Knowledge Acquisition Process. Knowledge/Ontological Analysis. Planning and selection of Intelligent/Statistical/Mathematical Methods/Techniques. Construction of Models and implementation of Techniques. Module Integration. Validation of Models/Techniques. Comparison of Techniques. Proposed Solution.

5. Intelligent System Project Output: Executive Summary. Project System Documentation: User's Manual, System Manual. Project Schedule (Gantt's Chart). The Project Time Sheet.

6. Intelligent Methods and Models: Review of main Intelligent Methods available.

7. Software tools: Review of main software tools available.

Learning Resources
Literature References
Internet Resources

Course material

The “Racó de la FIB” is the Virtual Campus for you. There, you will find any announcement, change in the schedule, the project related information, the results of the evaluation of the project and final marks, etc. There you will also find a pointer to this WEB page of the course where you could find some other material (slides, internet references, etc.) in electronic format

2021/2022 Sylabus:

1. Introduction
2. Problem Analysis
3. Definition of the ISP Issues
4. Development of an Intelligent System Project
5. Intelligent System Project Output
6. Intelligent Methods and Models
7. Software Tools

  The Project
The Intelligent System project will be developed by each teamwork of 4 or 3 people.
The project has 4 milestones (MS1: Definition of the Project, MS2: Midterm Reporting, MS3: Final software and documentation, MS4: Public Project Exposition and Defense) and the five associated deliverables of the project are:

MS1-D1 – Definition of the Project Document
MS1-D1 is due on: October 7
th, 2021

MS2-D2 – Midterm Document
MS2-D2 is due on: November 18
th, 2021

MS3-D3 – Final Document
Software Delivery
MS3-D3 and MS3-D4 are due on: January 19
th, 2022

MS4-D5 – Presentation slides document for the Public defense of the project
MS4-D5 is due on January 20
th, 2022

The final presentation and oral exposition of the Project, with the presence of all team members, will be done on January 20th, 2022 (30 min. for each group) with the attendance of one Business professional (CEO, CTO, CFO, etc.) of a real ICT/AI company

  Past ISP Projects








The assessment of the achievement of the objectives of the course will be made by assessing the achievements of an Intelligent System project throughout the course, which will be done working in teams of 4 or 3 students.

The final grade (FGrade) is a weighted average between the teamwork assessment
(TGrade) and the evaluation of the work of each individual student (IGrade) according to the formula:

FGrade = 0.5 * TGrade + 0.5 * IGrade

The individual grade for each student (IGrade) will be obtained as the mean between the observation and assessment of the ongoing work and participation of each student throughout the project according to the teacher (TeachA) and the self-assessment of each student participation and work in the team by the team members (SelfA). Thus,

IGrade = 0.5 * TeachA+ 0.5 * SelfA

The teamwork grade (TGrade) will be a weighted average between four marks, corresponding to the four Milestones, related to the definition of the project document (MS1-D1Gr), the midterm delivery of system analysis and design (MS2-D2Gr) the final document and software delivery (MS3Gr = 0.5*MS3-D3Gr + 0.5*MS3-D4Gr), and the final public presentation of the project (MS4Gr = 0.5*TechAss + 0.5*BusAss). The TechAss is the technical assessment of the MS4 by the teacher and the BusAss is the Business assessment of the project made by a CEO or similar position belonging to a real company. It will be computed according to the formula:

TGrade = 0.15 * MS1-D1Gr + 0.2 * MS2-D2Gr + 0.45 * MS3Gr + 0.2 * MS4Gr

Honour Code (adapted from Stanford University Honor Code)

A.    The Honor Code is an undertaking of the students, individually and collectively:
  1. that they will not give or receive aid in examinations; that they will not give or receive unpermitted aid in class work, in the preparation of reports, or in any other work that is to be used by the instructor as the basis of grading;
  2. that they will do their share and take an active part in seeing to it that others as well as themselves uphold the spirit and letter of the Honor Code.
B.    The faculty on its part manifests its confidence in the honor of its students by refraining from proctoring examinations and from taking unusual and unreasonable precautions to prevent the forms of dishonesty mentioned above. The faculty will also avoid, as far as practicable, academic procedures that create temptations to violate the Honor Code.

C.    While the faculty alone has the right and obligation to set academic requirements, the students and faculty will work together to establish optimal conditions for honorable academic work.

Examples of conduct which have been regarded as being in violation of the Honor Code include:

•    Copying from another’s examination paper or allowing another to copy from one’s own paper
•    Unpermitted collaboration
•    Plagiarism
•    Revising and resubmitting a quiz or exam for regrading, without the instructor’s knowledge and consent
•    Giving or receiving unpermitted aid on a take-home examination
•    Representing as one’s own work the work of another
•    Giving or receiving aid on an academic assignment under circumstances in which a reasonable person should have known that such aid was not permitted

In recent years, most student disciplinary cases have involved Honor Code violations; of these, the most frequent arise when a student submits another’s work as his or her own, or gives or receives unpermitted aid. The standard penalty for a first offense or multiple violations will be proposed by the teachers in charge of the course, and approved by the Academic Commission of the Master MAI.

Resources for student learning


greenball Basic References

greenball Complementary References

Internet Resources
Catalan Statistics Institute (Institut d'Estadística de Catalunya) 
European Statistics Office
UCI Machine Learning Repository
Knowledge Discovery Network of Excellence
Data Mining Resources
Data Mining Sites
GESCONDA software 
WEKA Software
R Software
RapidMiner Software