Intelligent System Project (ISP)


Master in Artificial Intelligence (UPC-URV-UB)

Fall semester, Course 2019/2020

(3 ECTS, 27 hours)


Responsible Professor:

KEMLG

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
miquel@cs.upc.edu

Professors:

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

Timetable:

Day Hour Room
Professor
Thursday
14-16 BUILDING A5, A5201
M. Sŕnchez-Marrč

Course schedule:

ISP lecturing: from September 12th 2019 to December 12th 2019

Holidays: October 24th, 2019 (no lecture), December 23rd 2019 until January 6th 2020 (Christmas Holidays)

        Change of schedule: October 31st, 2019 has a Friday schedule

        Starting date for the course: September 12th 2019

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

2019/2020 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 3 or 4 people.
The milestones of the project will be:

PM1 – Definition of the Project Document
PM1 is due on: October 3
rd, 2019

PM2 – Midterm Document
PM2 is due on: November 14
th, 2019

PM3 – Final Document and Software Delivery
PM3 is due on: January 8
th, 2020

PM4 – Public defence of the Project
PM4 will be on January 9
th, 2020

The final presentation and oral exposition of the Project will be done on January 9th, 2020 (30 min. approx. for each group) with the attendance
         of one Technical professional of some real company

 Past ISP Projects

13/14
14/15

15/16

16/17

17/18


18/19

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 3 or 4 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 = tp * TGrade + ip * IGrade, where the team percentage (tp) and the individual percentage (ip) ranges from 0.3 ≤ tp ≤ 0.5 and 0.7 >= ip >= 0.5, will be determined at the beginning of each course.

The individual grade for each student (IGrade) will be obtained by observing and assessing the ongoing work and participation of each student throughout the project, according to the teacher.

The teamwork grade (TGrade) will be a weighted average between four marks related to the definition of the project document (PM1Gr), the midterm delivery of system analysis and design (PM2Gr) the final document and software delivery (PM3Gr), and the final public presentation of the project (PM4Gr). It will be computed according to the formula:

TGrade = 0.1 * PM1Gr + 0.2 * PM2Gr + 0.5 * PM3Gr + 0.2 * PM4Gr



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

Literature

greenball Basic References

greenball Complementary References

Internet Resources

Catalan Statistics Institute (Institut d'Estadística de Catalunya)
http://www.idescat.net 
European Statistics Office
http://www.europa.eu.int/comm/eurostat
UCI Machine Learning Repository
http://archive.ics.uci.edu/ml/
Knowledge Discovery Network of Excellence
http://www.kdnet.org/
Data Mining Resources
http://www.scd.ucar.edu/hps/GROUPS/dm/dm.html
Data Mining Sites
http://www.cacs.louisiana.edu/~arlab/repos/sortedSites.html
GESCONDA software
http://www.lsi.upc.edu/~webia/KEMLG/projects/gesconda.html 
WEKA Software
http://www.cs.waikato.ac.nz/~ml/
R Software 
http://www.r-project.org/
RapidMiner Software
https://rapidminer.com 
Python 
https://www.python.org/