COURSE: DEGREE: YEAR: TERM: La asignatura tiene 25 sesiones

COURSE:
DEGREE: YEAR:
TERM:
La asignatura tiene 25 sesiones que se distribuyen a lo largo de 14 semanas. En cuatro de ellas habrá dos profesores
.
WEEKLY PLANNING
WEEK
SESSION
DESCRIPTIO
N
GROUPS
(mark X)
LECTURES
1
1
SEMINARS
WEEKLY PROGRAMMING FOR STUDENT
Indicate
YES/NO
If the
session
needs 2
teachers
DESCRIPTION
Course
introduction
No
HOMEWORK HOURS
(Max. 7h week)
Bibligraphical review
Reading: Machine Learning.
Tom Mitchell, McGraw Hill.
1997. Chapter 1:
Introduction
2
Introduction
to machine
CLASS
HOURS
1,6
X
1
SPECIAL
ROOM FOR
SESSION
(Computer
class room,
audio-visual
class room)
X
No
Additional Reading: Artificial
1,6
7
Página 1 de 9
Intelligence, S. Russel y P.
Norving. Prentice Hall,
2003.: Chapter 18: Learning
from Observations.
Secciones 18.1 y 18.2
Advanced Reading: The
Discipline of Machine
Learning. Tom M. Mitchell.
July 2006. CMU-ML-06-108
learning and
inductive
learning
Reading: Machine Learning.
Tom Mitchell, McGraw Hill.
1997. Páginas 52-66,
Chapter 3: Decision Tree
Learning
2
Reading: Aprendizaje
Automático. Daniel Borrajo
Millán, Jesús González
Boticario y Pedro Isasi
Viñuela. Sanz y Torres 2006.
Chapter 6. Técnicas
inductivas mixtas. Section
6.1
3
Decision
trees: ID3
2
4
X
No
Tutorial 1:
Introduction
to the
practical case
1,6
X
3
5
1,6
No
Evaluation of
decision
trees: crossvalidation
Development of Tutorial
Reading: Machine Learning.
Tom Mitchell, McGraw Hill.
1997. Páginas 66-78,
Chapter 3: Decision Tree
Learning
X
No
Additional Reading: Artificial
Intelligence, S. Russel y P.
7
1,6
7
Página 2 de 9
Norving. Prentice Hall,
2003. Chapter 18: Learning
from Observations. Section
18.3
Advanced Reading: Machine
Learning. Tom Mitchell,
McGraw Hill. 1997. Chapter
5: Evaluating Hypothesis
3
6
Tutorial 2:
Data preprocess and
classification
tools
1,6
X
No
Development of Tutoriall
Reading: Machine Learning.
Tom Mitchell, McGraw Hill.
1997. Section 4.4: El
perceptrón.
4
Reading: Machine Learning.
Tom Mitchell, McGraw Hill.
1997. Section 8.3: Locally
Weighted Regression
7
Linear
regression
and decision
trees
X
4
8
Tutorial 3:
Regression
and batch
evaluation
X
No
No
Reading: Aprendizaje
Automático. Daniel Borrajo
Millán, Jesús González
Boticario y Pedro Isasi
Viñuela. Sanz y Torres 2006.
Chapter 6. Técnicas
inductivas mixtas. Section
6.2
Development of Tutorial
7
1,6
1,6
Página 3 de 9
Clasificación
Bayesiana:
Naive Bayes
5
9
X
5
10
No
Practical case
1 (first
session)
X
6
11
7
12
13
No
Instance
based
Learning
X
6
Reading: Machine Learning.
Tom Mitchell, McGraw Hill.
1997. Secciones 6.1 a 6.11
Practical case
1 (second
session)
No
X
No
Development of practical
case
Reading: Machine Learning.
Tom Mitchell, McGraw Hill.
1997. Chapter 8
Development of practical
case
Reading: Aprendizaje
Automático. Daniel Borrajo
Millán, Jesús González
Boticario y Pedro Isasi
Viñuela. Sanz y Torres 2006.
Chapter 8. Técnicas de
aprendizaje por agrupación
no supervisada.
Unsupervised
learning:
clustering
1,6
1,6
7
1,6
1,6
7
1,6
Reading: Machine Learning.
Tom Mitchell, McGraw Hill.
1997. Chapter 6: Bayesian
Learning. Section 6.12: The
EM Algorithm
X
No
Additional Reading: An
7
Página 4 de 9
Algorithm for Vector
Quantizer Design .Yoseph
Linde and André Buzo and
Robet M. Gray. IEEE
Transactions on
Communications, Vol1.
Com-28, Nº 1. 1980.
Advanced Reading: Vector
Quantization and Signal
Compression . Allen Gersho
and Robert M.Gray. Kluwer
Academic Publishers. 1992.
k-means++: The
advantages of careful
seeding. Proceedings of the
eighteenth annual ACMSIAM symposium on
Discrete algorithms. 1027–
1035.
7
8
14
15
Practical
Case 1 (third
session)
Reinforcemen X
t Learning
X
No
No
Development of Practical
Case
Reading: Aprendizaje
Automático: conceptos
básicos y avanzados. Basilio
Sierra Araujo. Pearson
Prentice Hall. 2006. Chapter
11
1,6
1,6
7
Lectura Alternativa:
Machine Learning. Tom
Mitchell, McGraw Hill. 1997.
Chapter 13
Advanced Reading:
Reinforcement Learning: a
Survey. Lelie Pack Kaelbling
and Michael L. Littman and
Andrew W. Moore.
Página 5 de 9
International
Journal of Articial
Intelligence Research 4,
1996, pp 237-285
Advanced Reading:
Reinforcement Learning: an
introduction. R. Sutton y A.
Barto. The MIT Press. 1998
Tutorial 4:
Clustering
and
visualization
tools
8
16
1,6
X
9
17
18
Development of tutorial
eading: Aprendizaje
Automático: conceptos
básicos y avanzados. Basilio
Sierra Araujo. Pearson
Prentice Hall. 2006. Chapter
17. Combinación de
clasificadores
Ensemble of
classifiers
9
No
Practical case
2 (first
session)
X
X
No
No
7
1,6
Advanced Reading:
Ensemble Learning. Tomas
Dietterich. The Handbook of
Brain Theory and Neural
Networks. MIT Press. 2002
Development of practical
case
1,6
Página 6 de 9
10
19
Methodologic
al aspects of
machine
learning
X
10
20
No
Practical case
2 (second
session)
21
1,6
1,6
X
11
Reading: Aprendizaje
Automático. Daniel Borrajo
Millán, Jesús González
Boticario y Pedro Isasi
Viñuela. Sanz y Torres 2006.
Chapter 2: Fundamentos
No
Inductive
logic
programming
X
No
Development of Practical
Case
Reading: Aprendizaje
Automático. Daniel Borrajo
Millán, Jesús González
Boticario y Pedro Isasi
Viñuela. Sanz y Torres 2006.
Chapter 5: Enfoques mixtos
puramente simbólicos.
Section 5.3: Programación
Lógica Inductiva
7
1,6
Practical case
2 (third
session)
11
22
1,6
X
12
23
Machine
learning for
problem
solving
X
No
No
Development of practical
case
Reading: Aprendizaje
Automático. Daniel Borrajo
Millán, Jesús González
Boticario y Pedro Isasi
Viñuela. Sanz y Torres 2006.
Chapter 4. Aprendizaje
Deductivo
1,6
7
7
Página 7 de 9
Tutorial 5:
Reinforcemen
t Learning
12
24
1,6
X
13
25
Solving
machine
learning
problems
1,6
X
13
26
No
Development of practical
case
No
Practical
Case (first
session)
1,6
X
Yes
Development of practical
case
7
Practical case
(second
session)
14
27
1,6
X
14
14
28
29
Yes
Solving practical cases and
exam problems
Practical
case (third
session)
Yes
Development of practical
case
1,6
X
Practical
case 3 (Forth
session)
X
Yes
Development of practical
case
1,6
7
Subtotal
1
Total 1
(Hours of
class plus
student
48,33
146,33
Página 8 de 9
98
homework
hours
between
weeks 114)
15
16
Tutorials,
handing
in, etc
Assessmen
t
10
3
20
17
18
Subtotal
2
Total 2
(Hours of
class plus
student
homework
hours
between
weeks 1518)
TOTAL
(Total 1 +
Total 2)
3
33
179,33
Página 9 de 9
30