Machine Learning: Theory and Application
03 — 14 AUGUST, 2020
The course is held online
Enjoy a 40% discount for the tuition fee of winter and summer on-campus programs in 2021*
*Available only for students who will have participated in the online courses of a relevant field of study in summer 2020.
Do you want to go deeper into data science? Then this course is for you!
Online lectures will be delivered synchronized as live talk with professors and groupmates. Records of classes will be available on SPbPU platform for 1 month after the course end.
The course introduces students to the theoretical foundations of machine learning and data science, as well as to the solution of real business problems with the help of computer vision, classification and regression algorithms. The optimal balance between theory and practice provides both a good foundation and the ability to apply knowledge in practice.
ECTS credits: 4.0
Participation fee: 21 400 Rubles
Upon successful completion of the course students will receive hard copies of certificates with ECTS credits mailed by post.
Socio-cultural program of extracurricular activities and networking events are included*:
- Online International Party;
- Online Pub Quiz;
- Online Interactive Campus Tour with international buddies from PolyUnion club;
- Online broadcasting of excursion to the Hermitage museum.
*All of the listed above activities will to take place but in case any of those will have to be cancelled, an alternative event will be offered to participants.
Deadline for registration: July 20, 2020.
- Good command of English. All classes and extracurricular activities are carried out in English. Knowledge of the Russian language is not required.
- Applicants are expected to have at least 2 years of University level studies.
Anaconda (free program)
Please, install the program before the course starts
The syllabus includes:
- Introduction to Artificial intelligence and Machine Learning;
- History of AI and state of the art;
- Python tools for Machine learning;
- Machine learning project structure;
- Supervised and unsupervised learning;
- Overfitting and underfitting;
- Model Validation techniques;
- Machine learning algorithms classification;
- Data processing techniques;
- Binary classification and logistic regression;
- Shallow Neural networks;
- Deep Neural networks;
- Convolutional Neural Networks Basics;
- Sequence models basics.
Request the application form and submit the application package via e-mail: email@example.com
Professors and lecturers:
- Ogul Unal - PhD, Institute of Computer Science and Technology, SPbPU; M-com Search Engine Optimization specialist”;
- Nikita Kudryashov – PhD, Institute of Computer Science and Technology, SPbPU; Gazprom-neft leading specialist.