Tech Heart
Harbour.Space University

All rights reserved. 2018

RADOSLAV
NEYCHEV

This course aims to introduce students to the contemporary state of Machine Learning and Artificial Intelligence. It combines theoretical foundations of Machine Learning algorithms with comprehensive practical assignments. The course covers materials from classical algorithms to Deep Learning approaches and recent achievements in the field of Artificial Intelligence. This course is accompanied by Deep Learning in Applications course (Module 12), which brings the most recent achievements in the field and their applications.

Programming assignments will be implemented in Python 3. PyTorch framework will be used for Deep Learning practice.

MACHINE LEARNING
RESERVE MY SPOT

As a result of the course, students will:
- Learn the main theoretical foundations of Machine Learning and Deep Learning
- Get familiar with various approaches to supervised and unsupervised problems
- Gain essential experience in data preprocessing, model development, fitting and validation
- Develop skills required in product development and applied research

DATE: 17 Feb - 6 Mar, 2020

DURATION: 3 Weeks

LECTURES: 3 Hours per day

LANGUAGE: English

LOCATION: Barcelona, Harbour.Space Campus

COURSE TYPE: Offline

WHAT YOU WILL LEARN
COURSE OUTLINE
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Session 1

Machine Learning general overview. Supervised and Unsupervised learning problem statements. Metrics. kNN.
Maximum Likelihood estimation. Naive Bayesian Classification.

Introduction, overview and metric algorithms

Session 2

Linear regression

Gauss-Markov theorem. L1 and L2 regularization. Matrix differentiation.

Session 3

Margin. Logistic regression. Multiclass classification strategies.

Linear classification

Session 5

Model construction and validation

Bias-Variance decomposition. Train-Validation-Test framework. Hyperparameters tuning.

Session 4

SVM, kernel trick. Linear algebra recap: eigendecomposition of a matrix, SVD. PCA

Linear classification & dimensionality reduction

Session 6

Construction procedure. Bootstrap recap. Bagging. Random Subspace Method. Random Forest. Out of Bag error.

Decision trees & ensembling methods

Radoslav Neychev is a data scientist with focus on Deep Learning and Reinforcement Learning techniques. He has worked on a variety of research (CERN LHCb, MIPT Machine Intelligence Lab, CC RAS) and industrial projects (Yandex, RaiffeisenBank) in different domains vary from particle identification problem to fraudulent transactions detection.

Radoslav graduated from Moscow Institute of Physics and Technology, majoring in Applied Mathematics and Machine Learning. Radoslav is reading lectures and organising practical classes at Russian top-tier universities, tech companies and summer schools.

SKILLS:

-Machine Learning

-Deep Learning
-Reinforcement Learning
-Python
-C++
-System & Virtualization
-Public Speaking

ABOUT RADOSLAV

RESERVE MY SPOT

MACHINE
LEARNING

DATE: 17 Feb – 6 Mar, 2020

DURATION:  3 Weeks

LECTURES: 3 Hours per day

LANGUAGE: English

LOCATION: Barcelona, Harbour.Space Campus

COURSE TYPE: Offline

VLADISLAV
GONCHARENKO

Vladislav Goncharenko is a data scientist specializing in modern Computer Vision and Deep Learning fields. He worked on Smart Shop project (similar to Amazon Go). Also developing brain signals classification system for mind-controlled VR games. His academic studies includes Parkinson’s disease prediction by analyzing exercises videos.

ABOUT VLADISLAV
HARBOUR.SPACE 

Harbour.Space is a university created by entrepreneurs for entrepreneurs. We focus on meeting the demands of the future, while traditional education providers are too often stuck in the past.

We’re one of the only European institutions completely dedicated to technology, design and entrepreneurship, and our interdisciplinary courses are taught by some of today’s leading professionals. Our aim is not only to equip students with the knowledge to take on the real world, but to nurture, create and shape tomorrow’s tech superstars.

 Learn more about Harbour.Space.

HARBOUR.SPACE UNIVERSITY
BIBLIOGRAPHY

SKILLS:

-Machine Learning
-Computer Vision
-Time Series Analysis
-Python
-C++