SEL
Lecturing period: From February 16th,
2023 to May 25th,
2023.
MAI Exams/Evaluation period: June 9-23, 2023.
Holidays/No Lecturing:April
6th 2023 (Easter),
April
20th 2022
(Midterm exams)
Change of schedule: none
affecting us
Contents:
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 Retrieval 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
Competence
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
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 9th,
2023
PW1 is due on March 30th,
2023
PW2 (Individual work) - Implementation and evaluation of an
ensemble of classifiers.
PW2 will be assigned on March 30th,
2023
PW2 is due on May 4th,
2023
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 4th,
2023
PW3 is due on June 14th,
2023
Public Presentation of PW3 on June 16th,
2023 (Friday: 10-13)
Evaluation
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:
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.
Learning Resources
Literature References
Basic
References
Machine Learning and Supervised
Learning
Mitchell, Tom M.
Machine Learning, McGraw-Hill Companies, 1997.
Murphy, Kevin P.
Machine Learning: a Probabilistic Perspective, MIT Press,
2012.
Alpaydin, Ethem.
Introduction to Machine Learning, MIT Press, 2014.
Hastie, Trevor;
Tibshirani, Robert; Friedman, Jerome. The Elements of
Statistical Learning: Data mining, Inference, and
Prediction, Springer, 2009.
CBR
foundations
R. Schank & R.
Abelson. Scripts, Plans and Understanding. Lawrence
Erlbaum Associates, 1977.
R.
Schank. Dynamic Memory: a Theory of Learning in Computers
and People. Cambridge University Press, 1982.
CBR general introduction
C. Riesbeck & R.
Schank. Inside Case-Based Reasoning. Lawrence Erlbaum
Publishers, 1989.
J.
Kolodner. Case-Based Reasoning. Morgan Kaufmann, 1993.
M.
M. Richter and R.O. Weber (2013). Case-Based Reasoning: a
textbook. Springer-Verlag, 2013.
B. López (2013). Case-Based Reasoning: a
Concise Introduction. Morgan & Claypool, 2013.
CBR introductory papers, reviews and special issues
R. Barletta. An
introduction to Case-Based Reasoning. AI Expert 6(8),
1991.
E. Simoudis. Special
issue on Case-Based Reasoning. International Journal of
Expert Systems 4(2), 1991.
S. Slade. Case-Based
Reasoning: a Research Paradigm. AI Magazine 42:55, 1991.
Special issue on
Case-Based Reasoning. IEEE Expert 7(5), October 1992.
J.L. Kolodner. An
Introduction to Case-Based Reasoning. Artificial
Intelligence Review 6(1), 3-34, 1992.
A. Aamodt & E.
Plaza. Case-Based Reasoning: Foundational Issues,
Methodological Variations and System Approaches. AI
Communications 7(1):39-59, 1994.
I. Watson & F.
Marir. Case-Based Reasoning: a Review. The Knowledge
Engineering Review 9(4):355-381, 1994.
D. Leake (Ed.).
Case-Based Reasoning. Experiences, Lessons, Future
Directions. The MIT Press, 1996
Complementary
references
M. Sànchez-Marrè.
Intelligent Decision Support Systems. Springer
International Edition, 2022. (Chap. 6)
Christopher M. Bishop.
Pattern Recognition and Machine Learning. Springer; Edición:
1st ed. 2006. Corr. 2nd printing 2011.
Sebastian Raschka. Python
Machine Learning. Packt Publishing, 2015.
Peter Harrington. Machine
Learning in Action. NY: Manning Publications, 2012.
R. Schank & R.
Abelson. Scripts, Plans and Understanding. Lawrence Erlbaum
Associates, 1977.
J.L. Kolodner. An
Introduction to Case-Based Reasoning. Artificial
Intelligence Review 6(1), 3-34, 1992.
l
CBR application and tool reviews
P. Harmon. Case-Based Reasoning III. Intelligent Software
Strategies 8(1), 1992.
K.D. Althoff, E. Auriol, R. Barletta & M. Manago. A
Review of industrial Case-Based Reasoning tools. An AI
Perspectives Report, AI Intelligence, 1995.
I. Watson. Case-Based Reasoning Tools: an Overview. In
Proceedings of the 2nd UK Workshop on Case-Based Reasoning,
pp. 71-88. LNAI-1020, Springer-Verlag, 1996.
I. Watson. Applying
Case-Based Reasoning. Techniques for Enterprise Systems.
Morgan Kaufmann, 1997.
l
CBR conferences and
workshops
J.L. Kolodner (ed.).
Proceedings of the DARPA Case-Based Reasoning
Workshop. Morgan Kaufmann Publishers, 1988.
K.J. Hammond (ed.).
Proceedings of the DARPA Case-Based Reasoning
Workshop. Morgan Kaufmann Publishers, 1989.
E.R. Bareiss (ed.).
Proceedings of the DARPA Case-Based Reasoning
Workshop. Morgan Kaufmann Publishers, 1991.
M.M. Richter, S. Wess,
K-D Althoff and F. Maurer (eds.). Proceedings of 1st
European Workshop on Case-Based Reasoning (EWCBR’93).
LNAI-837, Springer-Verlag, 1993.
P. Anick & E.
Simoudis (eds.). Case-Based Reasoning & Information
Retrieval: Exploring Opportunities for Technology Sharing.
AAAI Spring Symposia SS-93-07. AAAI Press, 1993.
D.W. Aha (ed.).
Case-Based Reasoning. AAAI Workshop Series WS-94-01, AAAI
Press, 1994.
J.-P. Haton, M. Keane,
& M. Manago (eds.). Proceedings of 2nd European
Workshop on Case-Based Reasoning (EWCBR’94). AcknoSoft
Press, pp. 145-157, 1994. LNAI-984, Springer-Verlag, 1994.
M.M. Veloso & A.
Aamodt (eds.). Proceedings of the 1st International
Conference on Case-Based Reasoning (ICCBR’95). LNAI-1010,
Springer-Verlag, 1995.
I. Smith & B.
Faltings (eds.). Proceedings of 3rd European Workshop on
Case-Based Reasoning (EWCBR’96). LNAI-1168,
Springer-Verlag, 1996.
D. Leake & E. Plaza
(eds.). Proceedings of the 2nd International
Conference on Case-Based Reasoning (ICCBR’97). LNAI-1266,
Springer-Verlag, 1997.
M. Keane & T. Veale
(eds.). Proceedings of 4th European Workshop on Case-Based
Reasoning (EWCBR’98). LNAI-1488, Springer-Verlag,
1998.
K-D Althoff, R. Bergmann
& K. Branting (eds.). Proceedings of the 3rd
International Conference on Case-Based Reasoning (ICCBR’99).
LNAI-1650, Springer-Verlag, 1999.
E. Blanzieri & L.
Portinale (eds.). Proceedings of 5th European Workshop on
Case-Based Reasoning (EWCBR’2000). LNAI-1898,
Springer-Verlag, 2000.
D.W. Aha & I. Watson
(eds.). Proceedings of the 4th International Conference on
Case-Based Reasoning (ICCBR’2001). LNAI-2080,
Springer-Verlag, 2001.
S. Craw & A. Preece
(eds.). Proceedings of the 6th European Conference on
Case-Based Reasoning (ECCBR’2002). LNAI-2416,
Springer-Verlag, 2002.
K.D. Ashey & D.G.
Bridge (eds.). Proceedings of the 5th International
Conference on Case-Based Reasoning (ICCBR’2003). LNAI-2689,
Springer-Verlag, 2003.
P. Funk & P.A.
González Calero. Proceedings of the 7th European Conference
on Case-Based Reasoning (ECCBR’2004). LNAI-3155,
Springer-Verlag, 2004.
H. Muñoz-Ávila & F.
Ricci (eds.). Proceedings of the 6th International
Conference on Case-Based Reasoning (ICCBR’2005). LNAI-3620,
Springer-Verlag, 2005.
Roth-Berghofer, Thomas;
Göker, Mehmet H.; Güvenir, H. Altay (Eds.). Proceedings of
the 8th European Conference on Case-Based Reasoning
(ECCBR’2006). LNAI-4106, Springer-Verlag, 2006.
(eds.). Proceedings
of the 7th International Conference on Case-Based Reasoning
(ICCBR’2007). LNAI-****, Springer-Verlag, 2007.
(eds.). Proceedings of
the 9th Europeanl Conference on Case-Based Reasoning
(ECCBR’2008). LNAI-****, Springer-Verlag, 2008.
(eds.). Proceedings of
the 8th International Conference on Case-Based Reasoning
(ICCBR’2009). LNAI-****, Springer-Verlag, 2009.
Both ECCBR and ICCBR joined in the new annual ICCBR
conferences
(eds.). Proceedings of
the 18th International Conference on Case-Based Reasoning
(ICCBR’2010 - ICCBR'2021). LNAI-****, Springer-Verlag,
2010-2021.
Responsible Professor:
Projects
Basic
References
Complementary
references