SMI - Center for Sensory-Motor Interaction

Stochastic Processes I

Welcome to the course webpage

 

 

COURSE DESCRIPTION
AIM: This course provides analytical tools for studying random phenomena in biomedical systems. Special emphasis is given to properties of random processes.
LEARNING OUTCOMES: See course curriculum for detailed intended learning outcomes.
STYLE AND ASSESSMENT: Each lecture is 4 hours: Classroom lectures for 2 h and exercises for 2 h.
EVALUATION: 3 hours written examination.
MAIN LITERATURE: Barkat, Mourad - Signal detection and estimation (2nd Edition)
 
COURSE PLAN AND MATERIALS
Lecture Lecturer Topic and Materials Literature Learning Expectation
Lecture 1 Ernest Nlandu Kamavuako Introduction to probability and random variables
Slides | Exercises | Solutions
Mourad - Page: 1-8; 12-22 Deep Learning
Lecture 2 Ernest Nlandu Kamavuako Moments and multi-dimensional random variables
Slides | Exercises | Solutions
Mourad - Page: 23-64 Deep Learning
Lecture 3 Ernest Nlandu Kamavuako Introduction to random processes and stationarity
Slides | Exercises | Solutions
Mourad - Page: 88-96; 141-152 Deep Learning
Lecture 4 Ernest Nlandu Kamavuako Properties of correlation functions
Slides | Exercises | Solutions
Mourad - Page: 153-155 Deep Learning
Lecture 5 Samuel Schmidt Some random processes
Slides | Exercises | Solutions
Mourad - Page: 156-173 Basic Knowledge
Lecture 6 Samuel Schmidt Power spectral density
Slides | Exercises | Solutions
Mourad - Page: 174-178
Biomedical Signal Analysis: A Case-Study Approach
Deep Learning
Lecture 7 Samuel Schmidt Linear time-invariant systems (Filtering)
Slides | Exercises | Solutions
Mourad - Page: 178-185 Deep Learning
Lecture 8 Samuel Schmidt Ergodicity
Slides | Exercises | Solutions
Mourad - Page: 186-188 Deep Learning
Lecture 9   Self Study
  Facilitation
Lecture 10 Ernest Nlandu Kamavuako
Samuel Schmidt
Exam simulation
Exam set| Solutions
  Facilitation
 
EVALUATION
Written (3 hours): The problems of the exam are similar to the exercises done during the course. Thus we recommend that you understand the exercises and solutions and be able to reflect on them.
 
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