Math403BKK
Math Refresher for Data Scientists
Faculty
Evgeniya Korneva
Senior Data Scientist at Monster
Course length
Duration
Total hours
Credits
Language
Course type
Fee for single course
Fee for degree students
Skills you’ll learn
Overview
To understand Machine Learning, one needs to be familiar with fundamental mathematical concepts from linear algebra, analytic geometry, calculus, optimisation, probability and statistics. These topics are traditionally taught in separate in-depth courses, often over several years. This self-contained course brings the key concepts from different fields of mathematics together, allowing students to revise or even learn them for the first time. Examples and assignments given in this course are especially related to problems in Machine Learning that will be discussed in future courses.
Learning highlights
- perform basic operations with vectors and matrices
- solve a system of linear equations
- find extrema of a uni- or a multivariate function
- compute a definite integral
- apply the gradient descent algorithm
- count the number of permutations and combinations with or without repetitions
- compute the (conditional) probability of an event and apply Bayes’ rule
- compute expected value and variance of a random variable
- estimate parameters of a statistical model using the method of maximum likelihood
- get familiar with PCA, linear regression, numerical integration
Course outline
15 classes
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.
Session 2
Matrices
- Matrix arithmetics. Determinant. Trace. Rank. Matrix norm. Matrix inverse.
Session 3
Systems of linear equations
- Gaussian elimination. Linear regression.
Session 4
Matrix decomposition
- Eigenvalues and eigenvectors. Principal Components Analysis.
Review
Session 5
Linear Algebra exam
Univariate functions
- Monotonicity. Convexity. Limit of a function.
Session 6
Extrema of a function
- First and second derivatives. Chain rule. Extrema.
- Constrained Optimization and Lagrange Multipliers.
Session 7
Integration
- Standard antiderivatives. Change of Variable and Integration by Parts. Definite Integral.
Improper Integrals.
- Numerical Methods of Integration.
Session 8
Multivariate Calculus
- Multivariate functions. Partial derivatives and gradients. Chain rule. Extrema.
Session 9
Optimisation
- Convex optimisation.
- Numerical optimisation. Gradient Descent.
Review
Session 10
Calculus & optimisation exam
Basic combinatorics
Session 11
Basic Probability
- (Conditional) probability and Independence. Bayes’ theorem.
Session 12
Discrete Random variable
- Expectation, variance, covariance and correlation. Common discrete distributions and their properties.
Session 13
Continuous random variables
- Density. Common continuous distributions and their properties.
Session 14
Statistics
- Descriptive vs inferential statistics. Parameter estimation. Method of maximum likelihood.
Session 15
Probability & Statistics exam
Methodology
Each class will be a mix of a lecture, where new material will be presented, and some examples are worked out, and an exercise session, where students will be solving pen-and-paper and programming exercises individually or in small groups. In addition, homework assignments will be given to the students regularly to practice their skills further.
Grading
Evgeniya was born in Moscow and got a bachelor’s degree in Applied Mathematics and Informatics from Higher School of Economics in 2015. She then moved to Belgium to continue her education at KU Leuven, where she got a master's degree in Artificial Intelligence. In October 2016, Evgeniya joined the DTAI research group as a doctoral researcher.
Apart from conducting research on Machine Learning, Evgeniya was teaching master’s courses on Fundamentals of AI and Data Mining. For three years in a row, she won The Best Teaching Assistant prize. Apart from that, Evgeniya volunteers as an instructor at summer schools and workshops, as well as creates educational content on programming and Machine Learning for several on-line learning platforms.
See full profileApply for this course
Math Refresher for Data Scientists
by Evgeniya Korneva
Total hours
45 Hours
Dates
Oct 04 - Oct 22, 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.