ФПН/ Науки про дані (магістри) / Архів

Performance engineering of software applications (1.22-23.PKN22/M)

Lecturer: Andrii Babii
Academic group: PKN21/M
Year of study: 1

Recommender Systems (1.22-23.PKN22/M)

Lecturer: Nazariy Perepichka
Academic group: PKN22/M
Year of study: 1

3D Computer Vision (1.22-23.PKN22/M)

Lecturer: James Pritts
Academic group: PKN22/M
Year of study: 1

Software Architecture for Data Science in Python (1.22-23.PKN22/M)

Lecturer: Tymchuk Yuriy
Academic group: PKN22/M
Year of study: 1

Corporate Finance (1.22-23.PKN22/M)

Lecturer: Andrii Zhovtanetskyi
Academic group: PKN22/M
Year of study: 1



Game Theory (1.22-23.PKN22/M)

Lecturer: Oleksii Ignatenko
Academic group: PKN22/M
Year of study: 1

Bayesian Methods (1.22-23.PKN22/M)

Lecturer: Kamil Dedecius
Academic group: PKN22/M
Year of study: 1



Time Series (1.22-23.PKN22/M)

Lecturer: Yarema Okhrin
Academic group: PKN22/M
Year of study: 1

Design Thinking (1.22-23.PKN22/M)

Lecturer: 
Academic group: PKN22/M
Year of study: 1

Data Visualization (1.22-23.PKN22/M)

Lecturer: Andriy Gazin
Academic group: PKN22/M
Year of study: 1

Introduction to Deep Learning (1.22-23.PKN22/M)

Lecturer: Artem Chernodub
Academic group: PKN22/M
Year of study: 1

"Introduction to Deep Learning" aims to provide an overview of general DL design principles and most common applications. Mainstream DL approaches, methods, and applications are considered and discussed. The course contains two homework assignments. Each assignment includes theoretical and practical tasks. In the theoretical part, you need to write mathematical equations which describe the neural network's forward and backward passes. The outcome of the practical part is code written in Python: you will be asked to develop the neural network "from scratch."


Teaching schedule

Module 1

  • 2.06.2023 (Friday): 12:00-15:00 CET 
  • 3.06.2023 (Saturday): 8:30-11:00 CET

Module 2

  • 17.06.2023 (Saturday): 08:30-11:30 CET
  • 29.06.2023 (Thursday): 8:00-11:00 CET

Module 3

  • 30.06.2022 (Friday): 8:00-11:00 CET
  • 1.07.2022 (Saturday): 8:30-11:30 CET


Homework assignments deadlines




Public Communications (1.22-23.PKN22/M)

Lecturer: 
Academic group: PKN22/M
Year of study: 1

Machine Learning (1.22-23.PKN22/M)

Lecturer: Dmytro Fishman
Academic group: PKN22/M
Year of study: 1

Startup Launchpad (1.22-23.PKN22/M)

Lecturer: Ivan Petrenko
Academic group: PKN22/M
Year of study: 1

Humanity, Ethics and Technology (1.22-23.PKN22/M)

Lecturer: Roman Nazarenko
Academic group: PKN22/M
Year of study: 1

Introduction to Startups (1.22-23.PKN22/M)

Lecturer: 
Academic group: PKN22/M
Year of study: 1

Introduction to Statistical Programming (1.22-23.PKN22/M)

Lecturer: Roman Kyrychenko
Academic group: PKN22/M
Year of study: 1

Python Programming for Data Scientists (1.22-23.PKN22/M)

Lecturer: Yurii Kaminskii
Academic group: PKN22/M
Year of study: 1

Project Management (1.22-23.PKN22/M)

Lecturer: 
Academic group: PKN22/M
Year of study: 1

Statistics and Econometrics (1.22-23.PKN22/M)

Lecturer: Yarema Ohrin
Academic group: PKN22/M
Year of study: 1



Introduction to Data Science (1.22-23.PKN22/M)

Lecturer: Oleksandr Romanko
Academic group: PKN22/M
Year of study: 1