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Studies
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Studies
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The Institute
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Math402

Math Refresher for Masters

Barcelona Campus
Oct 17, 2022 - Oct 28, 2022
This course focuses, through practical examples and assignments, on revising the necessary topics that will allow students to join future Machine Learning courses.
Barcelona Campus
Oct 17, 2022 - Oct 28, 2022

Faculty Profiles

Irina Rudenko

Irina Rudenko

Data Scientist at Yandex Self-Driving Group

Valery Marchenkov

Valery Marchenkov

Data Scientist at S7 Airlines. Visiting Lecturer at MISIS.

Course length

2 weeks

Duration

3 hours
per day

Total hours

30 hours

Credits

4 ECTS

Language

English

Course type

Offline

Fee for single course

€1000

Fee for degree students

€500

Skills you’ll learn

Problem solvingMaximum Likelihood MethodBasic Operations with Vectors and MatricesSystem of Linear EquationsDefinite IntegralGradient Descent Algorithm
OverviewCourse outlineCourse materialsPrerequisitesMethod & grading

Overview

Understanding Machine Learning requires fundamental knowledge in mathematical areas such as linear algebra, calculus, optimization, probability and statistics. The Math Refresher course focuses, through practical examples and assignments, on revising the necessary topics that will allow students to join future Machine Learning courses and gain thorough knowledge about modern Artificial Intelligence.

Learning highlights

  • Helping students acquire a solid foundation for key mathematical concepts
  • Possibility to understand Machine Learning algorithms.

Course outline

10 classes

Dive into the details of the course and get a sense of what each class will cover.
Monday
Tuesday
Wednesday
Thursday
Friday
Monday
1

Session 1

Euclidean spaces

  • Vectors. Scalar product. Norms, length and distances. Angles and Orthogonality.

Vector spaces

  • Linear independence. (Orthogonal) basis. The dimensionality of a space.
Tuesday
2

Session 2

Matrices

  • Matrix arithmetics. Determinant. Trace. Rank. Matrix norm. Matrix inverse.

Systems of linear equations

  • Gaussian elimination. Linear regression.
Wednesday
3

Session 3

Matrix decomposition

  • Eigenvalues and eigenvectorsPrincipal Components Analysis.
Thursday
4

Session 4

Matrix decomposition

  • Singular value decomposition (reduced SVD)
Friday
5

Session 5

Univariate functions

Monotonicity. Convexity. Limit of a function.

Extrema of a function

First and second derivatives. Chain rule. Extrema.

Midterm on Linear Algebra

Monday
6

Session 6

Optimization

  • Constrained Optimization and Lagrange Multipliers.
  • Convex optimization.

Numerical optimization. Gradient Descent.

Tuesday
7

Session 7

Basic Probability

  • (Conditional) probability and Independence. Bayes’ theorem.

Discrete Random variables

  • Common discrete distributions and their properties.
Wednesday
8

Session 8

Random variables properties

  • Expectation, variance, covariance and correlation.

Continuous Random variables

  • Density. Common continuous distributions and their properties.
Thursday
9

Session 9

Statistics

  • Descriptive vs inferential statistics. Parameter estimation. Method of maximum likelihood
Friday
10

Session 10

Final Review & Exam

Prerequisites

Basic knowledge of Mathematics and Programming paradigms (e.g. Python basics)is required. Previous courses on Linear Algebra, Calculus, Optimization, Combinatorics or Probability and Statistics are appreciated.

Methodology

The course will consist of three-hour sessions and self-study practical assignments. The sessions will contain both theoretical and practical parts, with the ratio depending on the covered topics.

Grading

The final grade will be composed of the following criteria:
50% - Homework assignments
25% - Theoretical tests
25% - Final exam
Irina Rudenko

Faculty

Irina Rudenko

Data Scientist at Yandex Self-Driving Group

Irina is a Data Scientist at Yandex Self-Driving Group with strong mathematical background.

She received a master’s degree in Applied Mathematics in 2020 and a bachelor’s degree with honors in 2018 from DIHT MIPT, the Department of Data Analysis (the basic organization – Yandex).

See full profile
Valery Marchenkov

Faculty

Valery Marchenkov

Data Scientist at S7 Airlines. Visiting Lecturer at MISIS.

Valery is a Data Scientist at S7 Airlines. He works on aircraft engines and the fleet's recorded data in terms of fuel efficiency and maintenance planning algorithms development, travelers purchase, flight data and recommender systems. He also works as a Practice Instructor for Machine Learning courses at the Moscow Institute of Physics and Technology (MIPT) and as a Deep Learning lecturer at the NUST MISIS.

Before that he worked as a Structural Analysis Engineer at Boeing, where he worked on airframe design, static strength and fatigue analysis of metal parts for prospective aircrafts.

See full profile

Apply for this course

Snap up your chance to enroll before all spaces fill up.

Math Refresher for Masters

by Irina Rudenko, Valery Marchenkov

Total hours

30 Hours

Dates

Oct 17 - Oct 28, 2022

Fee for single course

€1000

Fee for degree students

€500

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.