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Alex Dainiak was born in Moscow in 1985. He had his first encounter with programming in 1998 while studying a Pascal circle and discovered he loved it. After working for some time as a programmer, he turned to mathematics. Alex Dainiak now considers himself a professional tutor and applied mathematician rather than a programmer. Nevertheless, he still produces a reasonable amount of code from time to time and takes part in personal and collective software development projects.

Research/Academic Interests:
Graph Theory, Combinatorics, Data Visualisation, Discrete Optimisation

The course is designed to enable the students who pass the course do the following:

-  Formulate a discrete optimisation problem using precise notation.

-  Estimate if the problem is computationally tractable in terms of precise solution. If not, then what general heuristics one may apply to solve the problem.


-  Evaluate the quality of concrete heuristics using various measures.

SKILLS:

- Algorithms

- Computer Science

- Machine Learning

- Discrete Mathematics

- C++ 

Research

- Python

- Data Analysis

- Natural Language Processing

DATE: 28 Jan - 15 Feb, 2019

DURATION: 3 Week

LECTURES: 3 Hours per day

LANGUAGE: English

LOCATION: Barcelona, Harbour.Space Campus

COURSE TYPE: Offline

WHAT YOU WILL LEARN
ABOUT ALEX
HARBOUR.SPACE 

Combinatorics is the main theoretical background for computer science, in particular for data science. As combinatorics deals with finite structures it provides tools, concepts naturally fitting the programme’s goals. The module starts with elementary and advanced counting techniques that enable students to evaluate effectiveness of algorithms, resource requirements of data structures and manipulations. 

Then graph theory is introduced. Graphs provide a perfect language to formulate problems arising in connection with computational questions. Finally advanced combinatorial structures and problems are treated that help students in dealing with abstractions of the field and in avoiding pitfalls of of not being exact and precise enough.

A good theoretical background for data sciences and applied computer sciences is like a good foundation for a building, without that, it collapses.

ALEX DAINIAK
RESERVE MY SPOT

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.

HARBOUR.SPACE UNIVERSITY

DATE: 28 Jan – 15 Feb, 2019

DURATION:  3 Weeks

LECTURES: 3 Hours per day

LANGUAGE: English

LOCATION: Barcelona, Harbour.Space Campus

COURSE TYPE: Offline

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Harbour.Space University
Tech Heart
COURSE OUTLINE

Session 1

Classical problems in discrete optimisation:
Problems on graphs and networks, cover problems, bin packing, knapsack, scheduling. Quality metrics for approximate algorithms.

Session 2

Local search algorithms:
Pros and cons. Kernigan–Lin modification of local search (KL-heuristic).

Session 3

Tree problems:
Recap of minimum spanning tree (MST) problem. Steiner tree problem; application of metric closure.

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Session 4

Heuristics directly based on local search:
Simulated annealing and tabu search.

DISCRETE OPTIMISATION
DISCRETE
OPTIMISATION
BIBLIOGRAPHY
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