Intelligent Decision Support Systems (IDSS)

Master in Artificial Intelligence (UPC-URV-UB)

Fall semester, Course 2023/2024

(4.5 ECTS, 40.5 hours)

Responsible Professor:

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, 2^nd floor, office 203 Campus Nord	miquel@cs.upc.edu	93 413 78 41	To convene by e-mail
Karina Gibert	Nexus-II building, Ground floor, IDEAI-UPC office Campus Nord	karina.gibert@upc.edu	93 413 73 23	To convene by e-mail

Timetable:

Day	Hour	Room	Professor
Monday	14-17	B4 BUILDING, ground floor, B4002	M. Sànchez/K. Gibert

Course schedule:: Lecturing period: From September 7^th 2023 to December 22^nd 2023

Holidays: September 11^th2023 (festivity), September 25^th, 2023 (bank holiday), October 30^th, 2023 (Midterm exams period), December 25^th2023 - January 6^th 2024 (Christmas)

Final Exams/Works: From January 8^th 2024 to January 19^nd 2024
Schedule Changes: September 26^th2023 has a Monday schedule

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)

IDSS Architecture
IDSS Analysis and Design

Requirements, advantages and drawbacks of IDSS

IDSS Validation

Implementation of an IDSS in a computer (an example)

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 techniques
        Validation
            Graphical Validation Tools
            Validation methods for Predictive and Discriminant Models
            Validation methods for Descriptive and Associative Models

    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)

IDSS Applications
    Recommender Systems

Tools for IDSS Development
    Specific Tools
    General Tools
        R
        Python

Future Trends, Advanced Topics, Open Challenges in IDSS and Conclusions

Course material

2023/2024 Sylabus (delivered through the “Racó de la FIB”):

                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
              Part 10 - Model-driven: Expert-based Models (1)
              Part 11 - Model-driven: Expert-based Models (2)
Part 12 - Model-driven: Qualitative Reasoning
            Part 13 - Applications&Tools
              Part 14 - Conc-Trends-AdvTop-OpChall&Res

       Case Studies:

   Case Study 1: Customers' Relationship Management (CRM) / Fidelization Analysis
               Case Study 2: Textile Industry
               Case Study 3: Wastewater Treatment Optimal 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

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 assigned on: September 26^th, 2023
PW1 is due on: October 2^nd, 2023

PW2 (Individual work) - Reviewing the state of the art of Intelligent Decision Support Systems.
PW2 will be assigned on: October 16^th, 2023
PW2 is due on: November 6^th, 2023

Find in the Papers Archive the list of papers analyzed in previous editions of the course till course 2019-2020
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: November 11^th, 2023
PW3 is due on: January 7^th, 2024

PW3 public presentation & discussion on: January 8^th, 2024 (from 16pm to 19pm)

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 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:

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

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/

Intelligent Decision Support Systems (IDSS)

Fall semester, Course 2023/2024

(4.5 ECTS, 40.5 hours)

Resources for a good performance as a working team