Machine Learning: Theory and Application
JANUARY 18 — JANUARY 29, 2021
The course is held online
ENJOY A 40% DISCOUNT FOR THE TUITION FEE OF 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 winter 2021.
Do you want to go deeper to machine learning? Join this Winter School!
The outstanding Winter School “Machine Learning theory and application” is cut out for those who are keen on machine learning applications and development.
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.
Duration: 2 weeks
ECTS credits: 4.0
Participation fee: 270 Euro
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 Interactive Campus Tour
- Online broadcasting of excursion to the Hermitage museum;
- Online Pub Quiz.
*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: January 10, 2021.
- Elementary knowledge of programming skills;
- Basic Python skills (classes, conditional constructs, functions, loops, methods, syntax);
- Knowledge of basics of matrix operations and differentiation;
- Good command of English. All classes and extracurricular activities are conducted in English. Knowledge of the Russian language is not required;
- Applicants are expected to have at least 1 year of University level studies.
Request the application form via e-mail address: email@example.com
Anaconda (free program)
Please, install the program before the course starts.
The syllabus includes:
- Introduction to Artificial intelligence and Machine Learning;
- Brief History review and state of the art;
- Supervised and unsupervised learning;
- Overfitting and underfitting;
- Regularization in ML;
- Model Validation techniques;
- Machine learning algorithms classification;
- Data processing techniques;
- Machine learning application workflow;
- Hyperparameters tuning tactiques;
- Binary classification and logistic regression;
- Shallow Neural networks;
- Deep Neural networks;
- Convolutional Neural Networks Basics;
- Deep Sequential Neural Networks.
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.