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Machine Learning (A)

Credits Dept. Type Requirements
7.5 (6.0 ECTS) LSI
  • Elective for DIE
 IA - Prerequisite for DIE

Instructors

Person in charge:  Luis Antonio Belanche Muñoz (belanche@lsi.upc.edu)
Others:Javier Béjar Alonso (bejar@lsi.upc.edu)
Luis José Talavera Mendez (talavera@lsi.upc.edu)

General goals

The aim of this subject is to introduce students to the area of machine learning within the scope of artificial intelligence. The subject will focus on the techniques and algorithms that allow systems to propose models based on examples and to improve their performance based on experience. As a complement to the subject, students will be introduced to practical applications of its use in analysis and data mining.

Specific goals

Knowledges

  1. Scope of and need for automatic learning
  2. Theoretical and practical knowledge of the various fields of automatic learning
  3. Knowledge of current applications regarding automatic learning techniques

Abilities

  1. Analysis of the need for automatic learning techniques for complex problems
  2. Use of automatic learning tools

Competences

  1. Ability to solve problems through the application of scientific and engineering methods
  2. Ability to create and use models of reality.
  3. Ability to design and carry out experiments and analyse the results.

Contents

Estimated time (hours):

T P L Alt Ext. L Stu A. time
Theory Problems Laboratory Other activities External Laboratory Study Additional time

1. Introduction to machine learning
T      P      L      Alt    Ext. L Stu    A. time Total 
2,0 2,0 1,0 0 0 0 0 5,0
What is machine learning? Where and when can learning be usefully applied? Definition and introductory examples. Classification of machine learning methods.

2. Inductive Learning
T      P      L      Alt    Ext. L Stu    A. time Total 
10,0 10,0 5,0 0 2,0 30,0 0 57,0
Rule Induction Systems and decision trees. Instance-based learning and local regression. Bayesian learning, Naive Bayes and Bayesian networks. Unsupervised inductive learning.

3. Neural networks
T      P      L      Alt    Ext. L Stu    A. time Total 
12,0 12,0 6,0 0 2,0 30,0 0 62,0
Perceptron. Non-linear regression and the Multi-layer Perceptron. Radial basis function networks. Kohonen networks. Hopfield networks. Applications.

4. Other approaches
T      P      L      Alt    Ext. L Stu    A. time Total 
4,0 4,0 2,0 0 3,0 10,0 0 23,0
Reinforcement learning. Explanation-based learning.


Total per kind T      P      L      Alt    Ext. L Stu    A. time Total 
28,0 28,0 14,0 0 7,0 70,0 0 147,0
Avaluation additional hours 3,0
Total work hours for student 150,0

Docent Methodolgy

The methodology consists of setting forth the theory in classes, the solution of exercises in class and the practical application of the concepts learnt in class in the laboratory.

Evaluation Methodgy

Evaluation is based on a final exam, grading of course assignments, and a grade for lab work. The final exam will test students the theoretical knowledge acquired by students during the course. Lab grades will be based on students" reports and lab practical work carried out throughout the course. The grade for course assignments will be based on submissions of small problems set during the course.







The final grade will be calculated as follows:







Final grade= Exam grade * 0.4 + Grade for assignments * 0.3 + Lab Grade *0.3

Basic Bibliography

  • Mitchell, Tom Machine Learning, McGraw Hill, 1997.
  • Ian H. Witten, Eibe Frank Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann Publishers, .
  • Hertz, Krogh, Palmer Introduction to the Theory of Neural Computation, Addison Wesley, .
  • R. Hecht-Nielsen Neurocomputing, Addison Wesley, 1991.
  • Jose Hernandez, Ma Jose Ramirez, Cesar Ferri Introducción a la minería de datos, Pearson/Prentice Hall, 2004.

Complementary Bibliography

  • G. Briscoe, T. Caelli A compendium of machine learning, Ablex Pub. Corp, 1996.
  • Pat Langley Elements of Machine Learning, Morgan Kaufmann, 1996.
  • U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, R. Uthurusamy (Eds) Advances in Knowledge Discovery and Data Mining, AAAI Press, MIT Press, 1996.
  • Alpaydin, Ethem Introduction to Machine Learning, The MIT Press, 2004.

Web links

  1. Obrir nova finestra http://www.lsi.upc.es/~bejar/apren/apren.html


Previous capacities

Students must have taken the Artificial Intelligence course.



 
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