MACHINELEARNING

SERGEYKHOROSHENKIKH

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 YOUWILL 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.SPACEUNIVERSITY

**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 OUTLINE

**Session 1**

**Introduction**

**Session 4**

**Linear models**

**Session 3**

**Theory of learning-1****PAC-learning**

**Session 2**

**Decision trees**

MACHINELEARNING

BIBLIOGRAPHY

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.

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