Supervised and Experiential Learning (SEL)

Master in Artificial Intelligence

(4.5 ECTS - 112.5 h)

Spring semester, Course 2020/2021

Responsible Professor:


Miquel Sànchez i Marrè
Knowledge Engineering & Machine Learning Group
Dept. of Computer Science
Universitat Politècnica de Catalunya
Campus Nord-Building OMEGA, 2nd floor, office 203


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


Day Hour Room
10:00-13:00 On-line/A6 203
M. Sànchez-Marrè


SEL Lecturing period: From February 19th, 2021 to May 28th, 2021.

        MAI Exams/Evaluation period: June 1-22, 2021.

        Holidays:  April 2nd 2021 (Easter),  April 16th 2021 (Midterm exams),  May 7th 2021 (FIB Spring festivity)

        Change of scheduling: none

1. Machine Learning: Supervised and Unsupervised ML techniques

      Basic principles and classification of Machine Learning techniques

2. Important Challenges in Supervised Learning

     Quantity of data
     Quality of data: representability, imbalanced class distribution
     Overfitting & Underfitting of models
     Bias & Variance of models
     Feature relevance
         i. Reminder: Feature Selection vs Feature Weighting, Filters and wrappers
         ii. Feature weighting techniques
3. Supervised Learning techniques

      Rule-based Classifiers
         i. Decision Tree Classifiers (ID3, C4.5, CART). Pruning techniques
         ii. Classification Rule Classifiers (PRISM, RULES, CN2, RISE)
      Probabilistic/Bayesian Classifiers
         i. Bayes Optimal Classifier
         ii. Gibbs algorithm
         iii. Naïve Bayes Classifier
      Linear Predictors
         i. Linear Regression / Multiple Linear Regression
      Statistical Classifiers
         i. Linear Discriminant Analysis (LDA)
         ii. Logistic/Multinomial Regression
4. Diversification / Ensemble of classifiers
      a. Reminder: General scheme
      b. Random Forests
5. Evaluation Techniques
      a. Discriminant/Classification models
      b. Prediction/Regression models
6. Advanced Classification Challenges
      a. Multi-label classification
      b. Ordinal classification
      c. Imbalanced Dataset classification
      d. Using noise and diversification for improving classification
      e. Meta-Learning of classifiers
      f. Incremental Learning: Data stream/on-line learning
7. Experiential Learning

       Reminder: Fundamentals of Case-based Reasoning
           Cognitive Theories
           Basic Cycle of Reasoning

8. CBR Academic Demonstrators/Examples

9. CBR System Components
Case Structure
Case Library Structure
Adaptation (Reuse)
Evaluation (Repair)
Learning (Retain)

10. CBR Application
Supervision and Management of a WWTP: a complex real-world domain

11. CBR Development Problems
Performance criteria
    Space Efficiency
    Time Efficiency

12. Reflective Reasoning in CBR
Case Base Maintenance

13. CBR Applications and Development Tools
Industrial Applications
Software Tools

14. CBR Systems' Evaluation

15. Advanced Research Issues
Temporal CBR
Spatial CBR
Hybrid CBR Systems
CBR as a recommendation tool: Feature Weighting Algorithm Recomendation based on CBR


Learning Resources
Literature References
Internet Resources

Course Material

2020/2021 Sylabus:
ML and Supervised Learning
Experiential Learning


All students in the course must do three practical works (PW1, PW2 and PW3). The practical works will be:

PW1 (Individual work) – Implementation and evaluation of a rule-based classifier.
PW1 will be assigned on March 12
th, 2021
PW1 is due on April 9
th, 2021

PW2 (Individual work) - Implementation and evaluation of an ensemble of classifiers.
PW2 will be assigned on April 9
th, 2021
PW2 is due on May 14
th, 2021

PW3 (Group work) - A practical work to build a CBR prototype for a synthetic task in a concrete domain.
PW3 will be assigned on May 14
th, 2021
PW3 is due on June 16
th, 2021
Public Presentation of PW3 on June 18
th, 2021


Evaluation of the knowledge and skills obtained by the students will be assessed through three project works. The first two works (PW1 and PW2) will be on an individual basis and the third one (PW3) will be on a team group basis.

The individual works will consist on the implementation, application and evaluation of some supervised machine learning algorithms. The teamgroup work will consist on the design, implementation, application and validation of a Case-Based Reasoning project to solve a synthesis problem.

The final grade will be computed as follows:

FinalGrade= 0.25 * PW1Gr + 0.25 * PW2Gr + 0.5 * PW3Gr * WFstud,        where 0 ≤ WFstud ≤ 1.2

WFstud 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 WFstud = 1.

The individual works (PW1 and PW2) will be evaluated according to the quality of the software developed (0.6), the evaluation done (0.2) and the documentation delivered (0.2).

The PW3Gr will be computed as follows:

PW3Gr = 0.5 * TeachAss + 0.5 * SelfAss

where TeachAss is the teacher assessment of the teamwork evaluated according to:

- The methodology of the work (0.5)
- The quality of the report written (0.2)
- The quality of the oral exposition (both presentation and content assessed, as well as the ability to answer questions) (0.2)
- Planning, coordination and management of the team (0.1)

and SelfAss is the individual assessment of each student by all the members of his/her team.

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:

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.

Learning Resources
Literature References
Basic References
Machine Learning and Supervised Learning

CBR foundations

CBR general introduction

CBR introductory papers, reviews and special issues

Complementary references

CBR application and tool reviews
CBR conferences and workshops

Both ECCBR and ICCBR joined in the new annual ICCBR conferences

Internet resources
Internet CBR resources

Software Tools
Source Code
Code from Inside Case-Based Explanation and Inside Case-Based Reasoning
JColibri2 (Universidad Complutense de Madrid, GAIA group)
JColibri web page project
AIA CBR shell (University of Edinburgh)
        CASPIAN (University of Wales)
        Selection Engine (by Baylor Wetzel, 2000)
        IUCBRF (Indiana University CBR Framework)
        MyCBR (German Research Center for Artificial Intelligence, DFKI)
        CASUEL (Universität Trier)
        GESCONDA-CBR shell (Universitat Politècnica de Catalunya-BarcelonaTech, KEMLG group)

Data Repository
UCI Repository of ML Databases