Optimization Methods in Machine Learning
Faculty
Alex Dainiak
Associate Professor at Moscow Institute of Physics and Technology
Course length
Duration
Total hours
Credits
Language
Course type
Fee for single course
Fee for degree students
Skills you’ll learn
Overview
After you define a model in machine learning, you tune the model to the data at hand. Mathematically it usually just boils down to optimizing the fitness function of the model. Naturally, various mathematical optimization methods become an important part in your data scientist’s toolbox as soon as you start working with lots of data and complex models. Even if you do not implement optimization algorithms in your daily analyst’s routine, it is a good idea to be well informed of what goes under the hood when you fit your model, so that you make an informed decision on the parameters of the optimization algorithms and the choice of the algorithm itself. As a bonus, applications of mathematical optimization go well beyond machine learning, so the course material may help you in a computer science career in general.
Learning highlights
- The main goal of the course is to empower learners with knowledge about the optimization algorithms that essentially take the most computation time in the fitting and computation of the machine learning model. Thus, the learner can make informed decisions while choosing the ML model class, the fitting strategy and even the manual implementation.
Course outline
4 classes
Session 1
Course outline. Recap of fundamental mathematical concepts: derivatives, convexity, matrix computations.
Session 2
ML models: linear regression, logistic regression, generalized linear models. Regularization. SVM model. Neural net model.
Session 3
ML models continued: optimization framework.
Session 4
Implementing regularization in regression and matrix decompositions.
Prerequisites
This course is one of three in a wholistic series.
Students that have already taken MSL-111 and those with prior experience with HTML, CSS, and Javascript building simple web pages will be good candidates for this module.
Alex was born in Moscow in 1985. His first encounter with programming happened in 1998 at a Pascal circle and that was love at first sight (or, better said, first line of code).
Alex teaches math and programming since graduating from the Moscow State University.
See full profileApply for this course
Optimization Methods in Machine Learning
by Alex Dainiak
Total hours
45 Hours
Dates
Jan 27 - Feb 14, 2020
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.