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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
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 OUTLINESession 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 RADOSLAVRESERVE 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 VLADISLAVHARBOUR.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.
HARBOUR.SPACE UNIVERSITY
BIBLIOGRAPHY
SKILLS:
-Machine Learning
-Computer Vision
-Time Series Analysis
-Python
-C++
IURII EFIMOV
SKILLS:
-Python
-Deep Learning
-Computer Vision
Iurii Efimov is a Research Scientist majoring in fields of modern Deep Learning and Computer Vision. His research is focused on state-of-the-art deep learning methods adaptation to run them on mobile platforms with limited computational resources. Also, Iurii is a member of the core team working on Mobile Iris and Face Recognition projects at Samsung Research Russia. He has contributed to user biometric authentication systems for several Samsung flagship devices. His academic studies are focused on human biometric authentication and anti-spoofing.
ABOUT IURII