Course detail

Machine Learning

FEKT-MPA-MLRAcad. year: 2020/2021

Not applicable.

Learning outcomes of the course unit

Not applicable.

Prerequisites

Not applicable.

Co-requisites

Not applicable.

Recommended optional programme components

Not applicable.

Recommended or required reading

N. Buduma: Fundamentals of Deep Learning, O'Reilly Media, 2017 (CS)
Ch. M. Bishop: Pattern Recognition and Machine Learning, Springer, 2011
I. Goodfellow, Y. Bengio, A. Courville, F. Bach: Deep Learning, The MIT Press, 2016

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes

Not applicable.

Language of instruction

English

Work placements

Not applicable.

Course curriculum

1. Introduction to classification. Classification error, classifier testing.
2. Linear Classifiers - basic principles and methods.
3. Bayesian approach to classification. Naive Bayes classifier.
4. Selected types of neural networks: Kohonen, Hopfield and Boltzman.
5. Classification by support vector machine method.
6. Decision and regression trees and forests, random forests.
7. Methods for improving classifier properties (bagging, boosting).
8. Advanced methods of feature selection and visualization (mRMR, t-SNE).
9. Principles of deep learning, deep neural networks (NN) and basic building blocks.
10. Principles of deep NN learning. Hardware requirements.
11. Variants of deep NN, autoencoders, recurrent NN.
12. Application of classification tasks for processing of signals, images and bioinformatic data. Application examples.
13. Implementation packages for deep learning (Caffe, TensorFlow, Statistics and Machine Learning Toolbox for Matlab and others).

Aims

Not applicable.

Classification of course in study plans

  • Programme MPA-BIO Master's, 2. year of study, winter semester, 5 credits, compulsory

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

Exercise in computer lab

26 hours, compulsory

Teacher / Lecturer