DS206
Introduction to Machine Learning
Faculty Profiles
Iurii Efimov
Senior Researcher at Artec 3D
Ivan Provilkov
Head of Machine Learning at STAI
Nikolay Karpachev
Machine Learning Developer at Yandex
Course length
Duration
Total hours
Credits
Language
Course type
Fee for single course
Fee for degree students
Skills you’ll learn
Overview
Machine Learning is revolutionizing our world right now: Recommendation systems, Dialog systems, Computer vision algorithms, autonomous vehicles and much more. It has a huge impact on all aspects of our lives and will achieve even more influence in the nearest future.
Modern Machine Learning systems can be very complicated. Their development may include choosing the right data processing algorithms, designing an appropriate model and training pipeline, building quality validation schemes.
In this course, we will give you a basic knowledge of Machine Learning - a foundation on top of which you will grow your knowledge and skills in this topic.
This introductory course gives students the skills to find and analyze potential Machine Learning problems and provides many simple yet effective methods to solve them
After this course, you will know how to define and solve regression and classification problems with ML algorithms. You will understand where you should pay attention when building ML systems. We will introduce you to the basics of neural networks and their applications.
Learning highlights
- This course’s main objective is to introduce students to the basic elements of modern Machine Learning, including theoretical foundations and practical applications.
Course outline
15 classes
Class 1
- Lecture: Machine Learning applications overview. Probability and linear algebra recap.
- Seminar: Basic tools and python practice
Class 2
- Lecture: Optimization
- Seminar: Linear model & optimization method
Class 3
- Lecture & Seminar: Linear classification and logistic regression
Class 4
- Lecture: Multilabel classification, KNN, Naive Bayes
- Seminar: KNN practice
Class 5
- Lecture & Seminar: Decision Trees & Random Forests
Class 6
- Lecture & Seminar: Gradient Boosting
Class 7
- Lecture: Bias-Variance decomposition. Composition methods.
- Seminar: Blending and Stacking
Class 8
- Mid-term. Q & A.
Class 9
- Lecture & Seminar: Introduction to statistical natural language processing
Class 10
- Lecture & Seminar: Neural Networks basics
Class 11
- Lecture & Seminar: Neural Networks Optimization, Deep learning tricks
Class 12
- Lecture & Seminar: CNN’s
Class 13
- Lecture & Seminar: RNNs
Class 14
- Lecture: Unsupervised learning: clustering, dimensionality reduction (PCA, T-SNE)
Class 15
- Final exam
Prerequisites
Python, Probability theory (basic), Linear Algebra, Calculus (basic), Statistics (basic)
Introduction to Calculus 1
Nov 09 - Nov 27, 2020
David Zmiaikou
PhD, Lecturer at the Belarusian State University
Probability and Statistics: Theory and Implementation
Nov 30 - Dec 18, 2020
Andrey Khokhlov
Chief Researcher, IEPT RAS
Intro to Programming 1: Python
Nov 30 - Dec 18, 2020
Hossein Yousefi
Co-founder and CTO at Identi
Linear Algebra 2
Jan 11 - Jan 29, 2021
Mikhail Romanov
Senior Machine Learning Engineer, Yandex, Expert
Methodology
Our sessions consist of two parts: a lecture session with slides and theoretical materials followed by a practice session devoted to the discussed topic. The practice sessions will include programming tasks and interactive problem-solving on real-life examples. Throughout the course, multiple home assignments will enable students to get hands-on experience in implementing machine learning pipelines.
Grading
Iurii Efimov is a Research Engineer majoring in fields of modern Deep Learning and Computer Vision. His research is focused on state-of-the-art deep learning methods for 2D and 3D signal processing. Also, Iurii is a member of the core team working on 3D reconstruction algorithms at Artec 3D Lux. He has contributed to innovative AI features of latest Artec 3D software and hardware products. His academic studies and former industry experience are related to human biometric authentication and anti-spoofing.
See full profileIvan is a Data Science expert with both research and industrial experience. He graduated from the Moscow Institute of Physics and Technology with a specialization in Data Analysis. He has research experience in Natural Language Processing, Deep Learning, Uncertainty Estimation, and Machine Learning for physical experiments. He worked in several companies as a Data Scientist, and now he is consulting companies about Machine Learning solutions, Digitization, and Innovations. He did R&D projects in recommendation systems for financial and retail sectors, machine translation, automatic validation of mechanical parts, and knowledge graphs construction. He also teaches Machine Learning at the Moscow Institute of Physics and Technology.
See full profileNikolay Karpachev is a machine learning developer specializing in deep learning methods in natural language processing. He has graduated from the Moscow Institute of Physics and Technology, where he received an M.Sc. degree in Computer science. Since then, he has worked on a number of industrial projects in machine learning, among which are Yandex Translate service and Yandex Alice voice assistant.
Currently, Nikolay works at Yandex as a Machine Learning Developer. His main work focus is research and development of deep learning methods with application to machine translation and general text understanding. As part of that work, he is involved in building scalable machine learning pipelines and deploying them in highly effective production systems. In addition to industrial work, Nikolay does research and educational projects in ML. His current interests include probabilistic data filtering schemes, adaptive training methods and quality estimation in NLP.
See full profileApply for this course
Introduction to Machine Learning
by Iurii Efimov, Ivan Provilkov, Nikolay Karpachev
Total hours
45 Hours
Dates
Feb 01 - Feb 19, 2021
Fee for single course
€1500
Fee for degree students
€750
How to secure your spot
Complete the form below to kickstart your application
Schedule your Harbour.Space interview
If successful, get ready to join us on campus
FAQ
Will I receive a certificate after completion?
Yes. Upon completion of the course, you will receive a certificate signed by the director of the program your course belonged to.
Do I need a visa?
This depends on your case. Please check with the Spanish or Thai consulate in your country of residence about visa requirements. We will do our part to provide you with the necessary documents, such as the Certificate of Enrollment.
Can I get a discount?
Yes. The easiest way to enroll in a course at a discounted price is to register for multiple courses. Registering for multiple courses will reduce the cost per individual course. Please ask the Admissions Office for more information about the other kinds of discounts we offer and what you can do to receive one.