Learning objectives
The objective of this module is to provide students with the theoretical basis and practical knowledge of some relevant machine-learning algorithms, aimed at classifying data.
The methods described in the course will allow students to:
- learn and use inductive-learning algorithms
- learn and use neural nets and other algorithm classes for the supervised classification of data
- learn and use the main supervised and unsupervised clustering algorithms
Prerequisites
Entry-level courses in linear algebra and probability theory, such as those normally offered in the corresponding 3-year Laurea course, are necessary pre-requisites for this course.
Course unit content
Part 1: Introduction
Lesson 1: How to set up a machine learning experiment
Lesson 2: Learning-based classification
Part 2: Neural networks
Lesson 3: Introduction to neural networks
Lesson 4: Supervised and unsupervised learning
Lesson 5: Supervised learning: the Backpropagation algorithm
Lesson 6: Unsupervised learning and clustering
Lesson 7: Kohonen's self-organizing maps (SOM)
Lesson 8: Learning Vector Quantization
Part 3: Other learning-based classifiers
Lesson 9: Support Vector Machines
Labs:
Lab 1: WEKA
Lab 2: Classifiers in WEKA: Multi-Layer Perceptrons
Lab 3: SOM-based clustering
Full programme
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Bibliography
[1] C. W. Therrien, "Decision, estimation and classification" Wiley, 1989
[2] C. M. Bishop "Pattern Recognition and Machine Learning", Springer, 2006.
[3] R O Duda, P, E. Hart, D. G. Stork, "Pattern classification", 2nd Ed., Wiley, 2001
Teaching methods
Classroom teaching, 18 hours.
Labs, 6 hours.
Homework regularly assigned.
Assessment methods and criteria
A practical project will be assigned, whose results will be presented and discussed by the student both as a written report and as an oral presentation.
Other information
Office Hours
By appointment (Scientific Complex, Building 1, floor 2, email stefano.cagnoni[AT]unipr.it).
2030 agenda goals for sustainable development
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