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
Contents:
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 7th,
2021
MS2-D2 – Midterm Document
MS2-D2 is due on: November 18th,
2021
MS3-D3 – Final Document
MS3-D4 – Software
Delivery
MS3-D3 and MS3-D4 are due on: January 19th,
2022
MS4-D5 – Presentation slides document for the Public defense of
the project
MS4-D5 is due on January 20th,
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
13/14
Analysing and interpreting tweets
related to weather: talking about the past or about the
future, making some sentiment/mood analysis
Detecting talks about topics of interest
related to some business in the Linkedin Network (WhoTalk)
14/15
A prediction system for bike and spot
availabilities (Bicing predictor)
15/16
A recommendation engine for movies.
(BAGmovies)
Image Search Engine for same style
images
PCC - Parrot Communication with children
- An intelligent interaction system for children with
difficult emotion expressing skills, through a flying drone
with camera
16/17
An online dating system based on
"vk.com" (PartnerTIP)
17/18
Robust Euro Notes Classification
(Adversarial Attack-Defense)
Meeting the right people
A Classification System for fictional
stories
18/19
Answering Machine (Question Answering)
Finding Lost Pets
Cookit
Deduplication engine
19/20
Market Price Suggestion
Multi-Modal Emotion Classifier
Automatic Piano fingering through
data-driven knowledge
Gun Detection on images via MRCNN
Sharesio (Secure and convenient photo
sharing)
An Intelligent System for Stock Market
Prediction
20/21
Face Interpolator
Midi to Tab: Automatic
generation of guitar tablatures
Money tracker
Evaluation
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:
Honour Code
(adapted from Stanford University Honor Code)
A. The Honor Code is an undertaking of the
students, individually and collectively:
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;
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
Literature
Basic References
Schalkoff, Robert J.
Intelligent Systems: Principles, Paradigms and
Pragmatics. Jones and Bartlett Publishers,
2011, ISBN: 0-7637-8017-0.
Hopgood, Adrian A. Intelligent
Systems for Engineers and Scientists. CRC
Press, 2011, ISBN: 1439821208.
Negnevitsky, Michael. Artificial
Intelligence: A Guide to Intelligent Systems.
Addisson-Wesley, 2004, ISBN: 0-3212-0466-2.
Sŕnchez-Marrč, Miquel. Intelligent
Decision Support Systems. Springer
International Publishing, 2022, ISBN: 978-3-030-87789-7
Complementary References
Russell, Stuart and Norvig, Peter. Artificial
Intelligence - A Modern Approach. Prentice Hall,
2010, ISBN: 0-13-207148-7.
IEEE Intelligent Systems JournaL
International Journal of Intelligent
Systems
Applied Intelligence Journal. The
International Journal of Artificial Intelligence, Neural
Networks, and Complex Problem-Solving Technologies.
ACM Transactions on Intelligent
Systems and Technology (ACM TIST)
Internet
Resources
Catalan
Statistics Institute (Institut d'Estadística de Catalunya)