|
News
This section contains news regarding the operation of the course.
Please consult it regularly.
Presentation
The Machine Learning course is offered by DEEC,
in the 1st semester of every year, to the students of the
Master's programs in Electrical and Computer Engineering and in
Biomedical Engineering and of the PhD / DEA program in
Information Security. It is also open to students of other programs
and to voluntary students.
The course's objective is to transmit the theoretical basis of
machine learning, to introduce various types of learning systems,
including neural networks, support vector machines, decision trees and
some unsupervised learning systems, and to give lab practice on
the use of several of these systems.
The course is open to foreign students. All essential course
materials are in English. The classes will be given in English if
there is at least one student who requires it.
Faculty
Note: The e-mail addresses given above are in a form that is not
directly usable. To use them you should replace " - at - " with
"@".
Syllabus
- Concept of learning. Supervised and unsupervised learning
- The linear learning unit (adaline). The LMS algorithm and
gradient methods.
- Multilayer perceptrons.
- Rosenblatt's perceptron (brief reference).
- Training criteria. Gradient-based optimization. The
backpropagation algorithm.
- Fast training methods: Momentum term and adaptive step sizes.
- Deterministic and stochastic training. Ljung's convergence
conditions.
- Statistical aspects of supervised learning.
- Regression problems: Estimation of conditional means, medians
and percentiles.
- Classification problems: The Bayes classifier. The Naive Bayes
classifier. Estimation of posterior probabilities by means of
supervised-learning systems.
- The generalization problem.
- Concept of generalization.
- Stopped training with cross-validation.
- Concept of regularization. The most common regularization
functionals.
- Support vector machines.
- Concept of maximal-margin linear classifier. The associated
optimization problem.
- Non-separable problems. Nonlinear maximal-margin classifiers and
kernel methods.
- Decision trees.
- Concept of decision tree.
- Learning in decision trees. The ID3 algorithm.
- The generalization problem in decision trees. Pruning.
- Unsupervised learning.
- Clustering and vector quantization. The K-Means algorithm.
- Estimation of probability densities, and its applications. The
EM algorithm for Gaussian mixture densities.
- Principal components analysis. Concept of principal components
and its usefulness. Estimation of the principal components by
algebraic methods and by the Oja/Sanger rule.
Labs
Registration in the lab
The registration is made through the fenix system. The
registration will open on Friday 17 September (time to be announced). At that
time, the lab (room 5.15, on the 5th floor of the North
Tower) will be open, so that the students can use the lab's
computers for registration. A faculty member will be present to
assist with any special registration needs.
To register, log in on the fenix system, open this course's
page and select, in the left-hand menu, "Agrupamentos"
(Groups). Then click the group that corresponds to the lab shift
that you want to register in.
Students who wish to use the previous year's lab grades shouldn't
register for the lab in the current semester. Otherwise their
previous grades will automatically be disregarded.
The lab groups are formed by two students each. Students should
arrange their own grouping and register in pairs. Students who
register individually may have another student assigned to their
group, and may find that their work schedules are not very
compatible.
Generic information
Lab classes take place in room 5.15
(also called LSDC1), on
the 5th floor of the North Tower.
The lab assignments will be published,
with an advance of about a week, in the
Labs Page.
You should prepare your lab work at
home, by getting acquainted with the assignment and by answering the
questions that don't require actual lab work.
The lab reports should be handed in at
the end of the corresponding lab sessions.
In most cases the reports are made
by just filling the appropriate blank spaces in the assignment. You
shouldn't exceed the blank spaces that are given in the assignment.
The purpose of this rule is to limit the size of the reports.
Reports that exceed the space that is given will have their grades
penalized.
Lab works are performed using the
Matlab software. A free quasi-clone of Matlab is Octave.
Doubts Sessions
To avoid a waste of faculty time, the
students who wish to attend a doubts session must send an e-mail message
to the corresponding professor until 19:00 of the day before the
session, and must be present at the time appointed for the session.
- Prof. Luís B. Almeida - Thursdays at 14:00 at his office,
room 9.19 of the North Tower.
Ask the security officer, at the entrance hall of the North Tower,
to announce you, to get access to the 9th floor.
- Prof.ª Margarida Silveira - Tuesdays at 14:00 in room
5.15, 5th floor of the North Tower.
Grading
- The course's grade is the average of the grades of the
lab, with a weight of 50%, and of the exam, also with a weight of
50%. The grades of both the lab and the exam must be at least 9.5.
- The exam has a duration of three hours. The students may take a
sheet of paper, of size A4, with contents of their choice. Only
non-alphanumeric calculators are allowed. Use of phones or of any
other communication equipment is not allowed, not even for use as
calculators or as clocks.
- The students may use, in the current semester, the lab grades
from the previous year (but not from earlier years). Students who
wish to use the previous year's lab grades shouldn't register for
the lab in the current semester. Otherwise their previous grades
will automatically be disregarded.
Grades:
Bibliography
Problems and exams
Current semester's exams
Previous semesters' exams
Problems
|