MACHINE LEARNING
SERGEY KHOROSHENKIKH
We offer innovative university degrees taught in English by industry leaders from around the world, aimed at giving our students meaningful and creatively satisfying top-level professional futures. We think the future is bright if you make it so.
Machine learning has proven its applicability for wide range of applications such as web search, self-driving cars or speech recognition. For many real-world problems (consider, for example, image classification), the state-of-the-art solutions are learned from data instead of being implemented explicitly as computer programmes.
The course covers both theoretical foundations and practical techniques of machine learning. The former is based on preceding topics of overall Data Science programme curriculum: calculus, probability, linear algebra. The latter will be helpful in subsequent modules: Python for massive data analysis, Mapreduce (large-scale machine learning), Machine Learning – 2, Text Mining, Neural Networks.
Sergey Khoroshenkikh is a senior software engineer with 5 years of experience in applied machine learning and data analysis. He graduated from Moscow Institute of Physics and Technology in 2015, and now he is earning a PhD at Moscow Institute of Physics and Technology in the area of random geometric graphs.
Currently he works in R&D department at Yandex, developing large-scale machine learning solutions for web-advertising (which is the main source of company’s income by now).
After completing the course, the students will be able to:
- Recognise machine learning problems in real-world situations
- Train and properly evaluate machine learning models on real data
- Choose suitable machine learning algorithms for particular problems
SKILLS:
-Python programming language
-Calculus and optimisation
-Probability
-Linear algebra
ABOUT SERGEY
HARBOUR.SPACE WHAT YOU WILL LEARN
DATE: 29 Apr - 17 May, 2019
DURATION: 3 Weeks
LECTURES: 3 Hours per day
LANGUAGE: English
LOCATION: Barcelona, Harbour.Space Campus
COURSE TYPE: Offline
HARBOUR.SPACE UNIVERSITY
RESERVE MY SPOT
DATE: 29 Apr - 17 May, 2019
DURATION: 3 Weeks
LECTURES: 3 Hours per day
LANGUAGE: English
LOCATION: Barcelona, Harbour.Space Campus
COURSE TYPE: Offline
All rights reserved. 2017
COURSE OUTLINESession 1
Introduction
Session 4
Linear models
Session 3
Theory of learning-1
PAC-learning
Session 2
Decision trees
MACHINE LEARNING
BIBLIOGRAPHY
Machine learning has proven its applicability for wide range of applications such as web search, self-driving cars or speech recognition. For many real-world problems (consider, for example, image classification), the state-of-the-art solutions are learned from data instead of being implemented explicitly as computer programmes.
The course covers both theoretical foundations and practical techniques of machine learning. The former is based on preceding topics of overall Data Science programme curriculum: calculus, probability, linear algebra. The latter will be helpful in subsequent modules: Python for massive data analysis, Mapreduce (large-scale machine learning), Machine Learning – 2, Text Mining, Neural Networks.
