SUBMIT
Tech Heart
Harbour.Space University
MASTER'S MACHINE 
LEARNING
SERGEY NIKOLENKO

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 is one of the most rapidly developing, most useful for applications, and generally “hottest” fields of computer science. However, it is not simply a collection of ad hoc recipes but a precise science based on probability theory. In the course, we will look at some of the most widely used machine learning tools through a probabilistic lens, understanding the intuition behind them, their applicability, and how to choose between them and apply them in real world situations. The course is accompanied with practical data science, where we will train the models on practical datasets, using both standard libraries provided in the Python ecosystem and other tools.

Sergey Nikolenko is a computer scientist with wide experience in machine learning and data analysis, algorithms design and analysis, theoretical computer science, and algebra. He graduated from the St. Petersburg State University in 2005, majoring in algebra (Chevalley groups), and earned his Ph.D. at the Steklov Mathematical Institute at St. Petersburg in 2009 in theoretical computer science (circuit complexity and theoretical cryptography). Since then, Dr. Nikolenko has been interested in machine learning and probabilistic modeling, producing theoretical results and working on practical projects for the industry. He is currently employed at the Steklov Mathematical Institute at St. Petersburg and Higher School of Economics at St. Petersburg. Dr. Nikolenko has more than 100 publications, including top computer science journals and conferences and several books.

• Machine learning: probabilistic graphical models, recommender systems, topic modeling

• Algorithms for networking: competitive analysis, FIB optimization

• Bioinformatics: processing mass-spectrometry data, genome assembly

• Proof theory, automated reasoning, computational complexity, circuit complexity

• Algebra (Chevalley groups), algebraic geometry (motives).

Become well versed in modern probabilistic inference, a foundation of machine learning.

• Learn basic and generalized linear models, SVMs, models for unsupervised learning (clustering and HMMs), probabilistic graphical models and approximate inference for PGMs, basic reinforcement learning, and several case studies covering some application areas that give rise to other interesting models and algorithms.

Understand the probabilistic intuition behind all of these models, learn to apply them correctly.

• Learn to apply these skills to practical settings, analyzing real life datasets.learn to apply these skills to practical settings, analyzing real life datasets.

SKILLS:

- Machine learning

- Algorithms for networking

- Bioinformatics

- Mathematical Modeling

- Python

Session 1

Intro and Bayes theorem

Introduction. History of AI. Types of machine learning problems. Probability theory basics. Bayes’; theorem and maximal a posteriori hypotheses. Laplace’s rule of succession.

OUTLINE COURSE
ABOUT SERGEY
HARBOUR.SPACE 
WHAT YOU WILL LEARN

Session 2

Linear Regression

Gaussian distribution, its ML estimates. The multidimensional Gaussian. Linear regression and least squares. Least squares as an ML estimate for Gaussian noise.

Session 3

Intro and Bayes theorem

Overfitting. Regularization. Ridge regression and lasso regression. Bayesian view of linear regression: MAP and predictive distribution.

Session 4

Classification

Classification: 1-of-K representation, linear decision functions. Fischer's linear discriminant. Bayes theorem for classification. Logistic regression. Multiclass logistic regression and softmax. The Bayesian view of logistic regression.

Session 5

Support vector machines

Support vector machines. Linear separation and max-margin classifiers. Quadratic optimization. Kernel trick. SVM variations: ν-SVM, one-class SVM, SVM for regression.

BY SERGEY NIKOLENKO

START TRIAL SESSION

DATE: 13 Mar - 31 Mar, 2017

DURATION: 3 Weeks

LECTURES: 3 Hours per day

LANGUAGE: English

LOCATION: Barcelona, Harbour.Space Campus

COURSE TYPE: Offline

Session 6

Statistical decision theory

Regression function, optimal Bayesian classifier. Bias-variance-noise decomposition. Nearest neighbors and the curse of dimensionality

Session 7

Model selection

Model selection via Laplace approximations.
Bayesian information criterion. Examples.

Session 8

Model combination

How to construct ensembles of models. Bayesian model averaging. Bootstrapping and bagging. Boosting: AdaBoost, gradient boosting.

Session 9

Unsupervised learning

Clustering. The EM algorithm for clustering. Justification of the EM algorithm  Hidden Markov models and the Baum-Welch algorithm.

Session 10

Probabilistic graphical models

Probabilistic graphical models: basic idea, factorizations, d-separation. Directed and undirected models. Factor graphs. Inference on factor graphs. Belief propagation with message passing.

Session 11

Model selection

Model selection via Laplace approximations.
Bayesian information criterion. Examples.

Session 12

Model combination

How to construct ensembles of models. Bayesian model averaging. Bootstrapping and bagging. Boosting: AdaBoost, gradient boosting.

Session 13

Unsupervised learning

Clustering. The EM algorithm for clustering. Justification of the EM algorithm  Hidden Markov models and the Baum-Welch algorithm.

Session 14

Probabilistic graphical models

Probabilistic graphical models: basic idea, factorizations, d-separation. Directed and undirected models. Factor graphs. Inference on factor graphs. Belief propagation with message passing.

Session 15

Theme: Reinforcement learning

Multiarmed bandits, exploration vs. exploitation. Confidence intervals. Minimizing regret: UCB1. Markov decision processes. On-policy and off-policy learning. TD-learning.

BIBLIOGRAPHY
HARBOUR.SPACE UNIVERSITY

START TRIAL SESSION

MASTER'S MACHINE 
LEARNING

@snikolenko

DATE: 10 Mar - 31 Mar, 2017

DURATION:  3 Weeks

LECTURES: 3 Hours per day

LANGUAGE: English

LOCATION: Barcelona, Harbour.Space Campus

COURSE TYPE: Offline

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