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
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
All rights reserved. 2017
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.
Session 4
Heuristics directly based on local search:
Simulated annealing and tabu search.
DISCRETE OPTIMISATION
DISCRETE OPTIMISATION
BIBLIOGRAPHY