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Educational guide | |||||||||||||||||||||||||||||||||||||||
IDENTIFYING DATA | 2024_25 | |||||||||||||||||||||||||||||||||||||||
Subject | INTRODUCTION TO INTELLIGENT SYSTEMS | Code | 00709016 | |||||||||||||||||||||||||||||||||||||
Study programme |
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Descriptors | Credit. | Type | Year | Period | ||||||||||||||||||||||||||||||||||||
6 | Compulsory | Second | Second |
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Language |
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Prerequisites | ||||||||||||||||||||||||||||||||||||||||
Department | ING.ELECTR.DE SIST. Y AUTOMATI |
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Coordinador |
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vgonc@unileon.es ralar@unileon.es ealeg@unileon.es fjaÑm@unileon.es |
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Lecturers |
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Web | http:// | |||||||||||||||||||||||||||||||||||||||
General description | This course covers the basic ideas of learing from examples, including the cycle of designing a classifier and how to evaluate these models. Supervised and unsupervised learning techniques are studied, including artificial neural networks and decision trees. | |||||||||||||||||||||||||||||||||||||||
Tribunales de Revisión |
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Competencias |
Code | |
A18108 | |
B5618 | |
B5619 | |
B5626 | |
B5627 | |
C4 | CMECES4 That students can transmit information, ideas, problems and solutions to both a specialised and non-specialised audience |
Learning aims |
Competences | |||
A18108 |
B5618 B5619 B5626 B5627 |
C4 |
Contents |
Topic | Sub-topic |
Block I: INTRODUCTION TO INTELLIGENT SYSTEMS. | FUNDAMENTALS Motivation, Basic principles, Approaches, Learning, Applications. CLASSIFIER DESIGN CYCLE Data collection, Feature selection, Model selection, Classifier tuning/training, Evaluation (metrics and techniques).Naive Bayes classifier. SUPERVISED LEARNING TECHNIQUES. Non parametric Supervised Learning: Fundamentals, Neighborhood classifiers. Parametric Supervised Learning: Fundamentals, Linear and logistic regression. MODEL EVALUATION AND SELECTION. Estimation techniques: cross-validation, hold-out, bootstrap, model comparison, classification performance metrics, other predictive model metrics: quadratic error, standard error. ARTIFICIAL NEURAL NETWORKS. Introduction, Topology and activation functions, The simple perceptron, The Adaline network, Multilayer perceptron (MLP). DATA PREPROCESSING. Anomalous data detection. Missing samples. Data integration and normalization. Data transformation. Feature selection and extraction. UNSUPERVISED LEARNING. Introduction, Space, distance and similarity, Partitioning and agglomerative techniques, Method of centroids (K-means). OTHER CLASSIFICATION MODELS. Support Vector Machines (SVM). Decision trees. Report - Practical applications Application of Intelligent Systems in Bioinformatics, Information retrieval systems, Spam detection, Fake news detection, Inappropriate content detection, Music analysis, Sentiment analysis and opinion data, Biometric recognition, Manufacturing environments, Industrial and environmental environments, Chemometrics, Intelligent laboratories, Cancer prediction models, Cybersecurity. |
Planning |
Methodologies :: Tests | |||||||||
Class hours | Hours outside the classroom | Total hours | |||||||
Problem solving, classroom exercises | 6 | 9 | 15 | ||||||
Laboratory practicals | 26 | 39 | 65 | ||||||
Lecture | 22 | 33 | 55 | ||||||
Mixed tests | 5 | 7 | 12 | ||||||
1 | 2 | 3 | |||||||
(*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students. |
Methodologies |
Description | |
Problem solving, classroom exercises | |
Laboratory practicals | |
Lecture |
Personalized attention |
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Assessment |
Description | Qualification | ||
Lecture | There will be a partial exam (25% of the overall grade) and a final exam (30% of the overall grade). Written tests: short answer, tests, multiple choice, problem solving, development. |
55% | |
Laboratory practicals | Attendance, group development and memory of the practical sessions will be evaluated (15% of the overall grade). At the same time as the theory exams, there will be a written test related to the practical contents (i.e., 10% of the overall grade in the partial exam and 10% of the overall grade in the final exam). |
35% | |
Group work, tutored and presented orally. | 10% | ||
Other comments and second call | |||
In the second call, there will be an exam (85% of the overall grade) and the attendance and development of the practical sessions will be evaluated (15% of the overall grade). |
Sources of information |
Access to Recommended Bibliography in the Catalog ULE |
Basic | |
Pattern Classification, 2nd Edition. Richard O. Duda, Peter E. Hart, David G. Stork. New York: John Wiley & Sons, 2001. Grokking Machine Learning, Luis Serrano, Manning Ed, 2022. Aprendizaje Automático: Conceptos Básicos y Avanzados. Basilio Sierra Araujo (coordinador). Prentice Hall. 2007. Aprende Machine Learning en Español: Teoría + práctica. Juan Ignacio Bagnato. ISBN:978-84-09-25816-1. 2020. |
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Complementary | |
Introduction to Machine Learning with Python. A guide for data scientists. Andreas C. Müller and Sarah Guido. O’Reilly, 2016. Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow. Géron, A. O'Reilly Media, Inc., 2022 Introduction to Machine Learning. 2nd Edition. Ethem Alpaydin. The MIT Press, 2010. MachineLearning.T. M. Mitchell,. New York: McGraw Hill. 1997. Combining Pattern Classifiers: Methods and Algorithms, L. Kuncheva, Wiley, Second Edition, 2014. |
Recommendations |