Person in charge: | (-) |
Others: | (-) |
Credits | Dept. | Type | Requirements |
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7.5 (6.0 ECTS) | CS |
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IA
- Prerequisite for DIE |
Person in charge: | (-) |
Others: | (-) |
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.
Estimated time (hours):
T | P | L | Alt | Ext. L | Stu | A. time |
Theory | Problems | Laboratory | Other activities | External Laboratory | Study | Additional time |
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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.
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T | P | L | Alt | Ext. L | Stu | A. time | Total | ||
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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.
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T | P | L | Alt | Ext. L | Stu | A. time | Total | ||
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4,0 | 4,0 | 2,0 | 0 | 3,0 | 10,0 | 0 | 23,0 | |||
Reinforcement learning. Explanation-based learning.
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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 |
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 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
Students must have taken the Artificial Intelligence course.