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Pattern Recognition - Statistical Learning
Lecturer
I. 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).
Level
Senior undergraduate course
Course Type
Semester Course
ECTS
5
Marking Scheme
1-10
Participation terms
Asynchronous on-line participation. Enrollment cut of date 25/02/2022.
Schedule
Spring semester. Duration 13 weeks. Typical start date: second half of February each year.
Language
English/Greek
Modality (online/in person):
Online
Notes
Maximum number of AIDA students: 5
