Supervised and Experiential Learning

You are here

This subject has not requirements, but it has got previous capacities
The course shows in detail two kind of Machine Learning techniques. The first part is devoted to Supervised Learning, and the second one to Experiential Learning. In the first part of the course the principles and challenges of Supervised Machine Learning will be analysed. Different kind of classification techniques (Bayesian, rule-based and statistical) will be studied. Also linear regression models will be explained. The use of diverse classification techniques will be analysed, and some methods will be detailed (random forests, etc.). Finally, some advanced topics in classification techniques will be discussed.Regarding the second part of the course, the basic components of the CBR reasoning cycle will be carefully studied and analysed. The application of CBR to the real world will be reviewed, and main problems the deployment of CBR application will be outlined.


Person in charge

  • Miquel Sanchez Marre ( )

Weekly hours

Guided learning
Autonomous learning


  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: representativity, 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 Rules 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. Classification models
    b. 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 learnin
  7. Experiential Learning
    Case-Based Reasoning
    1. Reminder: Fundamentals of Case-based Reasoning
    a. Cognitive Theories
    b. Basic Cycle of Reasoning
  8. CBR Academic Demonstrators/Examples
    Some examples will be anañysed.
  9. CBR System Components
    a. Case Structure
    b. Case Library Structure
    c. Retrieval
    d. Adaptation (Reuse)
    e. Evaluation (Repair)
    f. Learning (Retain)
  10. CBR Application on a real domain
    A real application will be described and analysed.
  11. CBR Development Problems
    a. Competence
    b. Space Performance
    c. Time Performance
  12. Reflective Reasoning in CBR
    a. Case Base Maintenance
  13. CBR Applications and Development Tools [2h]
    a. Industrial Applications
    b. Software Tools
  14. CBR Systems' Evaluation
    How to evalaute CBR systems will be analysed.
  15. Advanced Research Issues in CBR
    a. Temporal CBR
    b. Spatial CBR
    c. Hybrid CBR Systems
    d. Recommender Systems: CBR as a recommendation tool

Teaching methodology

The teaching methodology will include both theoretical lecture sessions, sessions with practical examples of the concepts and algorithms explained in the course, and also some sessions devoted to support the practical work of the students.

Evaluation methodology

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.



Previous capacities

The requirements are the ones provided by the mandatory courses of the Master, especially those provided by Introduction to Machine Learning (IML).