Course detail
Machine Learning and Recognition
FIT-SURAcad. year: 2020/2021
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.
Guarantor
Learning outcomes of the course unit
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
- recommended prerequisite
Co-requisites
Recommended optional programme components
Literature
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
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
Work placements
Aims
Specification of controlled education, way of implementation and compensation for absences
Classification of course in study plans
- Programme MITAI Master's
specialization NADE , any year of study, summer semester, 5 credits, elective
specialization NBIO , any year of study, summer semester, 5 credits, elective
specialization NGRI , any year of study, summer semester, 5 credits, elective
specialization NNET , any year of study, summer semester, 5 credits, elective
specialization NVIZ , any year of study, summer semester, 5 credits, elective
specialization NCPS , any year of study, summer semester, 5 credits, elective
specialization NSEC , any year of study, summer semester, 5 credits, elective
specialization NEMB , any year of study, summer semester, 5 credits, elective
specialization NHPC , any year of study, summer semester, 5 credits, elective
specialization NISD , any year of study, summer semester, 5 credits, elective
specialization NIDE , any year of study, summer semester, 5 credits, elective
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, elective
specialization NSEN , any year of study, summer semester, 5 credits, elective
specialization NVER , any year of study, summer semester, 5 credits, elective
specialization NSPE , any year of study, summer semester, 5 credits, compulsory
Type of course unit
Lecture
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
Fundamentals seminar
Teacher / Lecturer
Project
Teacher / Lecturer
Syllabus
- Individually assigned projects