Pattern Recognition – Statistical Learning


Ioannis Pitas,

Content and organization

Random variables and vectors. Decision functions. Classification algorithms utilizing decision functions. Classification based on distance. Classification based on Bayes decision theory. Artificial Neural Networks – Perceptron. Convolutional Neural Networks. Principal component analysis. Linear discriminant analysis. Estimation of probability distribution parameters. Analysis of similarity and web graphs. Vector quantization techniques. Programming assignments in C/C++ and MATLAB.

Course attendance will be asynchronous in English, using the educational material found in CVML Web Lectures modules:
Machine Learning
Neural Networks and Deep Learning

Compulsory bibliographical and/or programming assignments are foreseen to be carried out during the course.

In order to pass the course, you will be requested to:
1) study the course lecture pdfs (in English) and fill the respective understanding questionnaires:
2) do an obligatory bibliographical project (in ppt and tex)
3) do an obligatory programming project
4) participate in the final written course exams (remotely).


Senior undergraduate course

Course Duration

13 weeks, 2,5 hours/ week

Course Type

Semester Course



Marking Scheme

0-10 for written exams. Literature survey or programming assignments or mid-term exams or oral presentations provide an additional 2-4 points, if the exam mark is at least 4.

Participation terms

Asynchronous on-line participation.



Modality (online/in person):

Asynchronous on-line participation


Maximum number of AIDA students: 5

Host Institution
Aristotle University of Thessaloniki

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