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

13 weeks, 2,5 hours/ week

Semester Course

5

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.

Asynchronous on-line participation.

English/Greek

Asynchronous on-line participation

Maximum number of AIDA students: 5

https://qa.auth.gr/en/class/1/600191416