Course Name
Bioengineering models of information processing 
Course Overview 
Level of study	Master Degree	
Workload	ECTS: 3
Total Hours: 96
Contact Hours: 12
•	Lectures: 36
•	Labs: 36
•	Seminars: 12	
Course Code		
Semester	Winter,  Summer	
Prerequisites	Linear algebra, Mathematical optimization, Calculus, Programming (Matlab, C#, Python)	
Course Objectives	The aim of course is to give knowledge and practical skills of applying neural networks and genetic algorithms for solving various tasks.	
Learning Outcomes
	By the end of course students will know how to apply neural networks for: 
•	pattern recognition;
•	time-series data processing;
•	forecasting;
•	blind source separation;
•	classification;
•	clusterization	
Syllabus
	•	Neural network architecture
•	Backpropagation and gradient based training methods
•	Dynamic and recurrent neural networks
•	Backpropagation through-time
•	Genetic algorithms	
Labs
	•	Simple classification tasks
•	Time-series forecasting
•	Self-organizing maps for non-linear clasterization	
Projects	Comprehensive Analysis of Neural Networks Architecture for Solving Handwritten Digits Recognizing Problem	
Assessment	Exam	
Resources	Simon Haykin, Neural Networks a Comprehensive Foundation
Michael Nielsen, Neural Networks and Deep Learning
Ke-Lin Du, M. N. S. Swamy, Neural Networks and Statistical Learning	
Instructors	Dmitriy Devyatykh	
Примечание: пример описания дисциплины можно посмотреть по ссылке: http://www.ecs.soton.ac.uk/module/COMP6233#overview