Online | 2021
Training
for entering
IT universities
by Innopolis University
Training
for entering
IT universities
by Innopolis University
Online courses to prepare for admission to
IT universities for 9—11 grades school children
IT universities for 9—11 grades school children
Registration
Online training for entering Innopolis University and other IT universities
with the best experts of Innopolis University
The training is conducted in English so that you can fully immerse yourself in the learning environment (training at Innopolis University is conducted in English) and IT terminology.
Lessons from English-speaking educators, feedback, and live communication will help you successfully pass the tests for admission to Innopolis University and other IT universities,
as well as participate in competitions.
with the best experts of Innopolis University
The training is conducted in English so that you can fully immerse yourself in the learning environment (training at Innopolis University is conducted in English) and IT terminology.
Lessons from English-speaking educators, feedback, and live communication will help you successfully pass the tests for admission to Innopolis University and other IT universities,
as well as participate in competitions.
Introduction to Data Analysis machine learning
This course gives a general overview of Data Analysis and Machine Learning (ML). Currently, ML is very popular and it achieves remarkable performance when applied in different domains including but not limited to computer vision, natural language processing, and robotics. In this course, students will learn about the importance of data sets and how to take advantage of the increasing amount of public datasets for solving different tasks. The main goal of this course is to practice how to analyze and preprocess the data, and then apply different ML algorithms to make predictions in different domains. Working individually and in teams, students will learn about the ML pipeline, create different models and learn how to evaluate their models performance.
A computer scientist with design, support, administration, and implementation experience of IT projects and scientific work. Over 8 years of IT experience, currently working in the field of Artificial Intelligence.
Current Responsibilities: Studying the robustness of Deep Learning models under different Adversarial Attacks. Designing more robust Deep Learning Models using minimax optimization and Game theory. Applying robust models to the security field in general and malware detection in specific.
Educator: Bader Rashid
A computer scientist with design, support, administration, and implementation experience of IT projects and scientific work. Over 8 years of IT experience, currently working in the field of Artificial Intelligence.
Current Responsibilities: Studying the robustness of Deep Learning models under different Adversarial Attacks. Designing more robust Deep Learning Models using minimax optimization and Game theory. Applying robust models to the security field in general and malware detection in specific.
A computer scientist with design, support, administration, and implementation experience of IT projects and scientific work. Over 8 years of IT experience, currently working in the field of Artificial Intelligence.
Current Responsibilities: Studying the robustness of Deep Learning models under different Adversarial Attacks. Designing more robust Deep Learning Models using minimax optimization and Game theory. Applying robust models to the security field in general and malware detection in specific.
Educator: Bader Rashid
A computer scientist with design, support, administration, and implementation experience of IT projects and scientific work. Over 8 years of IT experience, currently working in the field of Artificial Intelligence.
Current Responsibilities: Studying the robustness of Deep Learning models under different Adversarial Attacks. Designing more robust Deep Learning Models using minimax optimization and Game theory. Applying robust models to the security field in general and malware detection in specific.
Mobile Development with Flutter app
In this course you will be learning all about the Flutter the Famous Cross platform for Mobile Development, Flutter is build and marinated by google and using it you can build beautiful application for iOS Android and the Web, and in this course you will learn:
Innopolis University, Researcher and TA 2020 - PRESENT
● Researcher and Doing a thesis for a Master degree on combining Mobile and ML through virtual wears-on
● Responsible teaching / creating laps for the following courses: Information theory, Data structure and Algorithm and Introduction to Programming
- Learn flutter and the Dart language without any prior experience
- Learn all about declarative programming
- Learn how to build layout and add interactivity
- Learn how to make navigation between screen and send data between them
- Learn how to create advanced UI and create grids and lists
- Learn how to build beautiful UI using Material Design component
- Learn how to link the screen and send data between it
- Learn how to deal with Data and Backend
- Learn about Threading in Dart
- Lean to deal with asynchronous and network programming
- Learn what is JSON and how to serialize it
- Learn how to program an advanced application and use design patterns.
- Learn Object Oriented Programming using Dart
- Learn how to make a class and use the object correctly
- Learn to use Flutter widgets stateless and stateful
- Learn about state management in flutter and how to handle them in a correct way
- Learn to apply some Flutter animations
- Understand and learn the basics of material design and how to use it properly in any application
- Learn to connect the application to the Internet and retrieve the data correctly and easily
- Learn to Run Flutter in iOS and Android
Innopolis University, Researcher and TA 2020 - PRESENT
● Researcher and Doing a thesis for a Master degree on combining Mobile and ML through virtual wears-on
● Responsible teaching / creating laps for the following courses: Information theory, Data structure and Algorithm and Introduction to Programming
Machine Learning for beginners
Nowadays, Artificial Intelligence (AI) and machine learning are used everywhere for multiple types of tasks such as: facial recognition (unlock your phone with your face), image classifications (identify different species of flowers), text translations (Google translate, Yandex translate), product recommendations (Movies recommendations on Netflix, yandex video, yandex taxi) etc. They are the most valuable skills to have right now. In this course, we'll learn the theory behind artificial intelligence, how machines learn, and some basic mathematical concepts necessary to build and train machine learning models.
The course contains two main parts:
In the first part, we'll explore different applications of machine learning algorithms and we will learn how to build your own models for facial recognition, image classifications, object detections etc, without coding. We will work on different interesting projects which have lots of fun. We will also guide those who have interesting ideas to implement their projects.
After completing the first part of the course, you will know all the fundamentals of machine learning, how it works and how to use it to solve real-world problems. The second part of the project will expose you to more details about the different concepts of machine learning. We will start by learning basic maths concepts (Linear algebra, Matrix manipulation etc.) that will be required to build a basic machine learning model from scratch. We'll explore models such as Linear Regression, Decision Tree, and K-nearest neighbour.
By the end of this course, you will have the necessary skills and knowledge required to build your own machine learning model with fun without writing codes. This will also enable you to master the details behind some popular machine learning algorithms, how they work , where they can be used and how they are trained.
Educator: Patrik Joslin Kenfack
Develop an expertise in AI ethics, contribute in the development of responsable IA and work in a challenging environment where I can polish my research, technical, and managerial skills.
Research Interests: Fairness in AI, Explainable AI; Deep Learning; Machine Learning, Transparency and Privacy; AI ethics; Data augmentation; Generative Models; Fair representation learning
The course contains two main parts:
In the first part, we'll explore different applications of machine learning algorithms and we will learn how to build your own models for facial recognition, image classifications, object detections etc, without coding. We will work on different interesting projects which have lots of fun. We will also guide those who have interesting ideas to implement their projects.
After completing the first part of the course, you will know all the fundamentals of machine learning, how it works and how to use it to solve real-world problems. The second part of the project will expose you to more details about the different concepts of machine learning. We will start by learning basic maths concepts (Linear algebra, Matrix manipulation etc.) that will be required to build a basic machine learning model from scratch. We'll explore models such as Linear Regression, Decision Tree, and K-nearest neighbour.
By the end of this course, you will have the necessary skills and knowledge required to build your own machine learning model with fun without writing codes. This will also enable you to master the details behind some popular machine learning algorithms, how they work , where they can be used and how they are trained.
Educator: Patrik Joslin Kenfack
Develop an expertise in AI ethics, contribute in the development of responsable IA and work in a challenging environment where I can polish my research, technical, and managerial skills.
Research Interests: Fairness in AI, Explainable AI; Deep Learning; Machine Learning, Transparency and Privacy; AI ethics; Data augmentation; Generative Models; Fair representation learning
Robotics
Do your students, preparing for the World Robot Olympiad or First Lego League Robot game, spend a lot of time looking for a good solution for a task? But even if they've got a solution, their robot can't guarantee the same result from run to run?
If you want your students to teach their robot to solve tasks effectively and reliably to get higher results at WRO, be welcome in this course! The course is based on Regular Category challenge, but participants can apply new skills also in other Categories.
Students learn following topics:
Students who are interested in robotics, their coaches and even parents can sign up for the WRO/FLL Rocket Science course. To pass the WRO/FLL Rocket Science you must have basic skills in working with LEGO Mindstorms EV3, and be able to program LEGO-robots using variables and arrays.
Course 2. RoboCup -
ages 9-16, 3 weeks, 9 lessons, ONLINE
The RoboCup community brings our future closer - future, where a team of fully autonomous humanoid robot soccer players shall win a soccer game. It is a great and important mission for humanity. We support this mission and believe that intelligent robots will make our future better. It's a long and adventurous way, but every long journey begins with a small step. That's why we introduce you a course about basic topics in mobile robotics. This course teacheslearns how to solve Rescue Maze and Rescue Line.
Students learn following topics:
Course 3. Computer Vision -
ages 15-19, 3 weeks, 9 lessons, ONLINE
Nowadays robots can't get along without a computer vision like humans without eyes. A lot of tasks can be easily solved using cameras and special recognition algorithms. Computer vision is widely used in different robotics competitions (EuroBot, Robot Soccer, RoboCup Industrial ...), where it helps to perform a mission in a more rapid and effective way.
Students learn following topics:
Educator: Patrik Joslin Kenfack
Develop an expertise in AI ethics, contribute in the development of responsable IA and work in a challenging environment where I can polish my research, technical, and managerial skills.
Research Interests: Fairness in AI, Explainable AI; Deep Learning; Machine Learning, Transparency and Privacy; AI ethics; Data augmentation; Generative Models; Fair representation learning
If you want your students to teach their robot to solve tasks effectively and reliably to get higher results at WRO, be welcome in this course! The course is based on Regular Category challenge, but participants can apply new skills also in other Categories.
Students learn following topics:
- Precise movements. Drive and turn smoothly and at a given angle or distance, avoiding typical errors and interference
- Reliable navigation. Navigate on the field fast and reliably following lines of different color and thickness without typical errors and interference
- Filters and sensoring. To work effectively with sensors to avoid interference by using multiple types of filters and to identify game objects
Students who are interested in robotics, their coaches and even parents can sign up for the WRO/FLL Rocket Science course. To pass the WRO/FLL Rocket Science you must have basic skills in working with LEGO Mindstorms EV3, and be able to program LEGO-robots using variables and arrays.
Course 2. RoboCup -
ages 9-16, 3 weeks, 9 lessons, ONLINE
The RoboCup community brings our future closer - future, where a team of fully autonomous humanoid robot soccer players shall win a soccer game. It is a great and important mission for humanity. We support this mission and believe that intelligent robots will make our future better. It's a long and adventurous way, but every long journey begins with a small step. That's why we introduce you a course about basic topics in mobile robotics. This course teacheslearns how to solve Rescue Maze and Rescue Line.
Students learn following topics:
- Sensors and vision
- Mapping and localization
- Aspects of Control theory
Course 3. Computer Vision -
ages 15-19, 3 weeks, 9 lessons, ONLINE
Nowadays robots can't get along without a computer vision like humans without eyes. A lot of tasks can be easily solved using cameras and special recognition algorithms. Computer vision is widely used in different robotics competitions (EuroBot, Robot Soccer, RoboCup Industrial ...), where it helps to perform a mission in a more rapid and effective way.
Students learn following topics:
- Image filtering and tracking.
- Geometric transformations, visual odometry, localization
- Objects recognition and classification
Educator: Patrik Joslin Kenfack
Develop an expertise in AI ethics, contribute in the development of responsable IA and work in a challenging environment where I can polish my research, technical, and managerial skills.
Research Interests: Fairness in AI, Explainable AI; Deep Learning; Machine Learning, Transparency and Privacy; AI ethics; Data augmentation; Generative Models; Fair representation learning
Mathematics
For studying computer science at a high level, sound knowledge of Mathematics is indispensable.
The course provides a possibility to refresh and improve knowledge of Mathematics studied at school. The course is practically oriented: the theory is briefly explained, and the focus is made on solving problems.
The topics included into the course correspond to the program on Mathematics covered at school in Russia. As the program may vary from country to country, the course is going to make up for a difference in programs if you are considering the possibility to study at a Russian university.
The course consists of 48 lectures (approximately 90 minutes each) provided with homework. The topics included are listed below.
1. Polynomials, rational fractions & their properties
2. Square roots and their properties; nth root
3. Introduction to number theory
4. Transformation of rational and irrational expressions.
5. Quadratic equations & their applications
6. Polynomial equations of higher degrees. Factorization of polynomials
7. Systems of linear equations
8. Systems of nonlinear equations (1)
9. Systems of nonlinear equations (2)
10. Rational inequalities
11. The absolute value of a number
12. Irrational equations
13. Irrational inequalities
14. Equations & inequalities with integer variables
15. Sequences. Arithmetic and geometric sequences
16. Word problems with arithmetic and geometric sequences
17. Plotting graphs of functions: lines, parabolas, hyperbolas, piecewise given functions, functions with absolute values
18. Sets of points in plane given by equations and inequalities
19. Trigonometric functions. Pythagorean trigonometric identity
20. Trigonometric formulae (1)
21. Trigonometric formulae (2)
22. Inverse trigonometric functions
23. Trigonometric equations
24. Systems of trigonometric equations. Trigonometric inequalities
25. Exponential function. Exponential equations and inequalities
26. Logarithms and their properties
27. Logarithmic equations
28. Logarithmic inequalities
29. Derivatives
30. Analyzing functions using derivatives
31. Tangent lines. Word problems with derivatives
32. Problems with parameters
33. Combinatorics. Rule of sum and rule of product. Inclusion — exclusion principle
34. Combinatorics: permutations, arrangements, combinations
35. Plane geometry. Triangles
36. Plane geometry. Circles (1)
37. Plane geometry. Quadrilaterals
38. Plane geometry. Pythagorean theorem
39. Plane geometry. Similarity
40. Plane geometry. Circles (2)
41. Plane geometry. Area
42. Plane geometry. Law of cosines
43. Plane geometry. Law of sines
44. Solid geometry. Polyhedra and their cross-sections
45. Solid geometry. Parallel lines and planes in space
46. Solid geometry. Perpendicular lines and planes in space
47. Solid geometry. Cones, cylinders and spheres
48. Plane & solid geometry. Coordinates and vectors
The course provides a possibility to refresh and improve knowledge of Mathematics studied at school. The course is practically oriented: the theory is briefly explained, and the focus is made on solving problems.
The topics included into the course correspond to the program on Mathematics covered at school in Russia. As the program may vary from country to country, the course is going to make up for a difference in programs if you are considering the possibility to study at a Russian university.
The course consists of 48 lectures (approximately 90 minutes each) provided with homework. The topics included are listed below.
1. Polynomials, rational fractions & their properties
2. Square roots and their properties; nth root
3. Introduction to number theory
4. Transformation of rational and irrational expressions.
5. Quadratic equations & their applications
6. Polynomial equations of higher degrees. Factorization of polynomials
7. Systems of linear equations
8. Systems of nonlinear equations (1)
9. Systems of nonlinear equations (2)
10. Rational inequalities
11. The absolute value of a number
12. Irrational equations
13. Irrational inequalities
14. Equations & inequalities with integer variables
15. Sequences. Arithmetic and geometric sequences
16. Word problems with arithmetic and geometric sequences
17. Plotting graphs of functions: lines, parabolas, hyperbolas, piecewise given functions, functions with absolute values
18. Sets of points in plane given by equations and inequalities
19. Trigonometric functions. Pythagorean trigonometric identity
20. Trigonometric formulae (1)
21. Trigonometric formulae (2)
22. Inverse trigonometric functions
23. Trigonometric equations
24. Systems of trigonometric equations. Trigonometric inequalities
25. Exponential function. Exponential equations and inequalities
26. Logarithms and their properties
27. Logarithmic equations
28. Logarithmic inequalities
29. Derivatives
30. Analyzing functions using derivatives
31. Tangent lines. Word problems with derivatives
32. Problems with parameters
33. Combinatorics. Rule of sum and rule of product. Inclusion — exclusion principle
34. Combinatorics: permutations, arrangements, combinations
35. Plane geometry. Triangles
36. Plane geometry. Circles (1)
37. Plane geometry. Quadrilaterals
38. Plane geometry. Pythagorean theorem
39. Plane geometry. Similarity
40. Plane geometry. Circles (2)
41. Plane geometry. Area
42. Plane geometry. Law of cosines
43. Plane geometry. Law of sines
44. Solid geometry. Polyhedra and their cross-sections
45. Solid geometry. Parallel lines and planes in space
46. Solid geometry. Perpendicular lines and planes in space
47. Solid geometry. Cones, cylinders and spheres
48. Plane & solid geometry. Coordinates and vectors
How to participate
Choose a course
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Fill in the participant form
Pay for participation
Payment guarantees your membership in the group
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Start as the group is formed
Advanced training
In programming, robotics
and the latest IT trends
and the latest IT trends
Live Communication
You will have access to chat with teachers and other participants
Implemented projects
You will create your project, which
you can develop further
you can develop further
Pumping up your English
Courses are taught only in English
to prepare you for an international environment
to prepare you for an international environment
Immersion in Terminology
You will be immersed in subject terminology to make it easier for you to pass your entrance exams
Innopolis University is a young IT university in Russia
and the intellectual center of the new city.
Innopolis University employs Russian and foreign specialists in information technology and robotics who teach in English.
The educational programs of Innopolis University are focused on business and industrial needs, aimed at creating a qualified workforce for the IT industry.
and the intellectual center of the new city.
Innopolis University employs Russian and foreign specialists in information technology and robotics who teach in English.
The educational programs of Innopolis University are focused on business and industrial needs, aimed at creating a qualified workforce for the IT industry.
We Are Cute
The smartest people work every day to provide the best service and make our clients happy
Bader Rashid
A computer scientist with design, support, administration, and implementation experience of IT projects and scientific work, Innopolis University
Moofie Imam
Researcher and TA 2020, Innopolis University
Researcher and Doing a thesis
for a Master degree on combining Mobile and ML through virtual wears-on.
Researcher and Doing a thesis
for a Master degree on combining Mobile and ML through virtual wears-on.
Patrick Kenfack
Develop an expertise in AI ethics, contribute in the development
of responsable IA and work in a challenging environment where
I can polish my research, technical, and managerial skills.
of responsable IA and work in a challenging environment where
I can polish my research, technical, and managerial skills.
Sergey Gorodetskiy
Associate Professor at Innopolis University
Our contacts
Phone: +7 (843) 203 92 53
Email: dovuz@innopolis.university
Email: dovuz@innopolis.university