Course detail
Machine Learning and Recognition
FIT-SURAcad. year: 2019/2020
The tasks of classification and pattern recognition, basic schema of a classifier, data and evaluation of individual methods, statistical pattern recognition, feature extraction, multivariate Gaussian distribution,, maximum likelihood estimation, Gaussian Mixture Model (GMM), Expectation Maximization (EM) algorithm, linear classifiers, perceptron, Gaussian Linear Classifier, logistic regression, support vector machines (SVM), feed-forward neural networks, convolutional and recurrent neural networks, sequence classification, Hidden Markov Models (HMM). Applications of the methods of speech and image processing.
Supervisor
Learning outcomes of the course unit
The students will get acquainted with the problem of machine learning applied to pattern classification and recognition. They will learn how to apply basic methods in the fields of speech processing and computer graphics. They will understand the common aspects and differences of the particular methods and will be able to take advantage of the existing classifiers in real-situations.
The students will get acquainted with python libraries focused on math, linear algebra and machine learning. They will also improve their math skills (probability theory, statistics, linear algebra) a programming skills. The students will learn to work in a team.
Prerequisites
Basic knowledge of the standard math notation.
Co-requisites
Not applicable.
Recommended optional programme components
Not applicable.
Recommended or required reading
Bishop, C. M.: Pattern Recognition, Springer Science + Business Media, LLC, 2006, ISBN 0-387-31073-8.
Hart, P. E., Stork, D. G.:Pattern Classification (2nd ed), John Wiley & Sons, 2000, ISBN: 978-0-471-05669-0.
Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 2016. Deep Learning. The MIT Press.
Planned learning activities and teaching methods
Not applicable.
Assesment methods and criteria linked to learning outcomes
- Mid-term test - up to 15 points
- Project - up to 25 points
- Written final exam - up to 60 points
Language of instruction
Czech
Work placements
Not applicable.
Aims
To understand the foundations of machine learning with the focus on pattern classification and recognition. To learn how to apply basic algorithms and methods from this field to problems in speech and image recognition. To conceive basic principles of different generative an discriminative models for statistical pattern recognition. To get acquainted with the evaluation procedures.
Specification of controlled education, way of implementation and compensation for absences
The evaluation includes a mid-term test, individual project, and the final exam. The mid-term test does not have a correction option, the final exam has two possible correction terms
Classification of course in study plans
- Programme BIT Bachelor's, 2. year of study, summer semester, 5 credits, optional
- Programme IT-BC-3 Bachelor's
branch BIT , 2. year of study, summer semester, 5 credits, optional
- Programme MITAI Master's
specialization NADE , any year of study, summer semester, 5 credits, optional
specialization NBIO , any year of study, summer semester, 5 credits, optional
specialization NGRI , any year of study, summer semester, 5 credits, optional
specialization NNET , any year of study, summer semester, 5 credits, optional
specialization NVIZ , any year of study, summer semester, 5 credits, optional
specialization NCPS , any year of study, summer semester, 5 credits, optional
specialization NSEC , any year of study, summer semester, 5 credits, optional
specialization NEMB , any year of study, summer semester, 5 credits, optional
specialization NHPC , any year of study, summer semester, 5 credits, optional
specialization NISD , any year of study, summer semester, 5 credits, optional
specialization NIDE , any year of study, summer semester, 5 credits, optional
specialization NISY , any year of study, summer semester, 5 credits, compulsory
specialization NMAL , any year of study, summer semester, 5 credits, compulsory
specialization NMAT , any year of study, summer semester, 5 credits, optional
specialization NSEN , any year of study, summer semester, 5 credits, optional
specialization NVER , any year of study, summer semester, 5 credits, optional
specialization NSPE , any year of study, summer semester, 5 credits, compulsory
Type of course unit
Lecture
26 hours, optionally
Teacher / Lecturer
Syllabus
- The tasks of classification and pattern recognition, the basic schema of a classifier, data sets and evaluation
- Probabilistic distributions, statistical pattern recognition
- Generative and discriminative models
- Multivariate Gaussian distribution, Maximum Likelihood estimation,
- Gaussian Mixture Model (GMM), Expectation Maximization (EM)
- Feature extraction, Mel-frequency cepstral coefficients.
- Application of the statistical models in speech and image processing.
- Linear classifiers, perceptron
- Gaussian Linear Classifier, Logistic regression
- Support Vector Machines (SVM), kernel functions
- Neural networks - feed-forward, convolutional and recurrent
- Hidden Markov Models (HMM) and their application to speech recognition
- Project presentation
Projects
13 hours, compulsory
Teacher / Lecturer
Syllabus
- Individually assigned projects
eLearning
eLearning: currently opened course