Math402
Math Refresher for Masters
Faculty Profiles
Irina Rudenko
Data Scientist at Yandex Self-Driving Group
German Chesnokov
Machine Learning Engineer at Yandex
Course length
Duration
Total hours
Credits
Language
Course type
Fee for single course
Fee for degree students
Skills you’ll learn
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
15 classes
Session 1. Linear Algebra.
Course overview and initial knowledge test.
Linear algebra basics.
- Vectors. Scalar product. Norms, length and distances. Angles and Orthogonality.
Vector spaces and Euclidean spaces.
Session 2. Linear Algebra.
Linear combinations and basis.
Change of basis.
Matrices as Linear transforms.
- Geometric interpretation of linear transforms. Matrix algebra.
Session 3. Linear Algebra
Matrices.
- Determinant. Trace. Rank. Matrix norm.
Systems of linear equations.
- Gaussian elimination. Number of solutions. Linear regression.
Session 4. Linear Algebra.
Matrix decomposition
- Eigenvalues and eigenvectors. Principal Component Analysis.
Session 5. Linear Algebra.
Matrix decomposition.
Singular value decomposition (reduced SVD).
Session 6. Calculus.
Linear algebra test.
- Univariate functions.
- Monotonicity. Convexity. Limit of a function.
Extrema of a function.
First and second derivatives. Chain rule. Extrema.
Session 7.Calculus.
Matrix calculus.
- Gradients for some common function
Integration.
Session 8. Optimization.
Optimization.
- Constrained Optimization and Lagrange Multipliers. Convex optimization. Numerical optimization. Gradient Descent.
Session 9. Probability theory.
Basic set theory and combinatorics.
- Number of permutations, combinations and partitions.
Calculus test.
Session 10. Probability theory.
Basic Probability.
- (Conditional) probability and Independence. Bayes’ theorem.
Discrete Random variables.
- Common discrete distributions and their properties.
Session 11. Probability theory.
Random variables properties.
- Expectation, variance, covariance and correlation.
Continuous Random variables.
- Density. Common continuous distributions and their properties.
Session 12. Statistics.
Statistics basics.
- Parameter estimation. Method of maximum likelihood.
Session 13. Statistics.
Probability theory test.
Statistics.
- Statistics for analytics: hypothesis testing, Z-test, t-test, non-parametric tests.
Session 14.
Fun lecture and final test preparation session.
- Most likely: Random graphs.
Session 15.
Final Review & Exam
Course materials
Books
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
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 profileI received my bachelors in Applied Mathematics at Higher School of Economics, Moscow. Data science has become my passion since it's a combination of my two favourite disciplines: mathematics and computer science. My industrial experience includes working in a startup as well as two large IT companies. I've started my career in the field of natural langue processing and later moved to self-driving cars. Now I work as a machine learning engineer in Scene Modelling group at Yandex Self-Driving Cars. I co-authored a paper accepted to NeurIPS 2021.
See full profileApply for this course
Math Refresher for Masters
by Irina Rudenko, German Chesnokov
Total hours
45 Hours
Dates
Oct 16 - Nov 03, 2023
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.