Intelligent Decision Support Systems (IDSS)


Master in Artificial Intelligence (UPC-URV-UB)

Fall semester, Course 2020/2021

(4.5 ECTS, 40.5 hours)




Responsible Professor:

KEMLG
Miquel Sànchez i Marrè
Knowledge Engineering & Machine Learning Group
Dept. of Computer Science
Universitat Politècnica de Catalunya · BarcelonaTech
Campus Nord - Building OMEGA, 2nd floor, office 203
miquel@cs.upc.edu




Professors:

Professor  Office
E-mail  Phone
Tutoring
Miquel Sànchez i Marrè OMEGA building,
  2nd floor, office 203
Campus Nord 
miquel@cs.upc.edu 93 413 78 41
To convene by e-mail
Karina Gibert
C5 building,
  2nd floor, office 219
Campus Nord
karina.gibert@upc.edu
93 413 73 23
To convene by e-mail



Timetable:

Day Hour Room
Professor
Monday
15-18 A5 BUILDING, first floor, A5101
M. Sànchez/K. Gibert



Course schedule:

Lecturing periodFrom September 14th 2020 to December 21th 2020
Holidays: October 12th, 2020 (festivity), November 9th, 2020 (Midterm exams period), December 23rd 2020 - January 6th 2021 (Christmas)

Schedule Changes: None



Contents:

lIntroducction
Complexity of real-world systems or domains
The need of decision support tools

Decisions
Decision Theory
Modelling of Decision Process

Evolution of Decision Support Systems 
Historical perspective of Management Information Systems
Decision Support Systems (DSS) 
Advanced Decision Support Systems
Intelligent Decision Support Systems (IDSS)

Intelligent Decision Support Systems (IDSS)
IDSS Architecture
IDSS Analysis and Design
Requirements, advantages and drawbacks of IDSS 
IDSS Validation
Implementation of an IDSS in a computer: Environmental Systems Management (an example)
l
The use of Intelligent Models in Decision Support
    Data-driven modelling
        Knowledge Discovery and Data Mining
        Artificial Intelligence and Statistics
        Data Structure
        Pre-processing techniques & post-processing
   
    Data-Driven Models
        Descriptive Models       
            (AI & Stats) Clustering
        Associative Models
            (AI) Association Rules
            (AI&Stats) Bayesian (Belief) Networks
        Discriminant Models
            (Stats) Logistic regression
        Predictive Models
            (AI) Case-Based Reasoning
                Instance-Based Learning (IBL)   
   
     Model-driven techniques
        Expert-based models
            Rule-based Reasoning
        Qualitative Reasoning models
            Qualitative reasoning approaches
            Causal Loops Diagrams (CLDs)

l
Post-processing and Model Validation
        Post-processing Techniques
        Validation
    Graphical Validation Tools
    Validation methods for Predictive and Discriminant Models
    Validation methods for Descriptive and Associative Models
Statistical Methods for Hypotheses Verification

Tools for IDSS Development
    Specific Tools
    General Tools
        R
        Python

Future Trends in IDSS and Conclusions

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 practical works, the results of the evaluation of the works 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

2020/2021 Sylabus:
                    Part 1 - Introduction
              Part 2 - Decision Theory
             
Part 3 - Evolution of DSS/IDSS
              Part 4 - Data-driven Modelling (1): ConceptuaL Map & Pre-processing
              Part 5 -
Data-driven Modelling (2): Post-Processing & Model Validation
              Part 6 - Data-driven: Descriptive Models
              Part 7 - Data-driven: Associative Models
                    Part 8 - Data-driven: Discriminant Models
              Part 9 - Data-driven: Predictive Models

 
       Case Studies:

               Case Study 1: Customers' Relationship Management (CRM) / Fidelization Analysis
               Case Study 2: Textile Industry
               Case Study 3: Optimal Wastewater Treatment Selection
               Case Study 4: Garden Design
               Case Study 5: Sustainable Action Planning
              



Relevant References:

Papers directly linked with the contents of the course:

Linked with Part 3: Preprocessing:
Survey on preprocessing
Missing imputation: The MiMMI method


Linked with Part 5 to 8: Intelligent Models in Decision Support
Data Mining Method Conceptual Map


Linked with Case Study 7:
Case 7 Bayesian Networks for Predictions


Linked with the working teams:
Assertive behaviour
Hitchhikers and Couch Potatoes



Practical exercises

Three practical works must be done by all students in the course (PW1, PW2 and PW3). The practical works are:

PW1 (Individual work) - Modelling a decision making process in a concrete domain.
PW1 will be given on: September 28th, 2020
PW1 is due on: October 5th, 2020

PW2 (Individual work) - Reviewing the state of the art of Intelligent Decision Support Systems.
PW2 will be given on: September 28th, 2020
PW2 is due on: October 26th, 2020

Find in the Papers Archive the list of papers analyzed in previous editions of the course till course 2017-2018
The collection of papers is here:
        IDSS-Papers Archive 1213
        IDSS-Papers Archive 1314
        IDSS-Papers Archive 1415
        IDSS-Papers Archive 1617
        IDSS-Papers Archive 1718
        IDSS-Papers Archive 1819
        IDSS-Papers Archive 1920

PW3 (Group work) - A practical work to build an Intelligent Decision Support System
PW3 will be given on: October 26th, 2020
PW3 is due on: January 10th, 2021
        PW3 public presentation & discussion on: January 11th, 2021

Resources for a good performance as a working team

Working Team resources
Turning Students groups into effective teams
Assertivity in working teams (adapted from [Oaklay2004])
Assertivity in working, Montañé
How to deal with dysfunctional members
Comunicación efectiva en el trabajo
 



Evaluation

Evaluation of the knowledge and skills obtained by the students will be assessed through the 3 practical Works. The final grade will be the weighted mean of the grade of each practical work: 

 FinalGr = 0.25*PW1Gr + 0.25*PW2Gr + 0.5*PW3Gr * WFst,  0 ≤ WFst ≤ 1.2

where WFst is a Working Factor evaluating the work of a particular student within his/her teamwork in PW3. It will be obtained by observing and assessing the load of work and degree of participation of each student throughout the PW3. In normal conditions, the WFst = 1

The PW1 will be evaluated by means of its quality and its justified explanation in the document. The PW2 will be evaluated according to its accuracy and completeness. The PW3 will be evaluated through the following formula:

PW3Gr = 0.4*MetGr + 0.2*DocGr + 0.2*OrEGr + 0.05*TManGr + 0.15*IGr

Where:
- MetGr: Grade for the quality of the methodology and work done
- DocGr: Grade for the documentation delivered
- OrEGr: Grade for the quality of the oral exposition (both presentation and content assessed, as well as the ability to answer
                 questions)
- TManGr: Grade for the planning, coordination and management of the team
- IGr: The individual evaluation of each student including her/his integration level within the team group. This individual student grade (IGr) will be a mean between the teacher assessment of the student (TeachA) and the self-assessment of the student participation by the other members of the team (SelfA). Thus,  IGr = 0.5*TeachA+ 0.5*SelfA




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

        Updated list of 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
RapidMiner
        https://rapidminer.com/
R Software 
        http://www.r-project.org/ 
WEKA Software
http://www.cs.waikato.ac.nz/~ml/
GESCONDA software
        http://kemlg.upc.edu/en/projects/gesconda-1/gesconda
Kaggle
        http://www.kaggle.com
Java
        http://www.oracle.com/technetwork/java/index.html
Python
        https://www.python.org/