Course List

Graph and network properties, small world phenomenon and distributions, probabilistic models (information flow), queuing systems and networks, markov chain and decision process, network optimisation and mathematical modelling, optimisation problems (convex, lagrangian, integer programming, heuristics, greedy algorithms).

Classifier design and methods, discriminant functions, learning algorithms, neural networks (artifical, multilayer, feedforward), deep discriminative and generative neural networks, feature extraction, support vector machines, clustering.

Analytical and numerical optimisation, random-based optimisation, binary genetic algorithms, continuous genetic algorithms, evolution strategies, ant colony optimisation, particle swarm optimisation, differential evolution.

Big Data technologies

Distributed software and database systems, MapReduce, OLTP/OLAP, Apache Spark, data streaming.

Software security, implementation errors and exploits, static and dynamic analysis, privilege management, access control.

Image formation foundations (physics, geometry, human visual system), filtering and convolution, feature detection, grouping and segmentation, object categorization and recognition.

Intelligent and autonomous agents, BDI, reactive and hybrid agent architectures, auctions and negotiations.

Knowledge representation, probabilistic reasoning, temporal probabilistic reasoning, sequential decision making, game theory, argumentation, machine learning.

Descriptive statistics, regression and surveys, hypothesis testing, association and estimation, experiments and clinical trials.

Linux fundamentals, virtual machines, and PostgreSQL.

Individual research, documentation tools and methods (research, synthesis, evaluation), literature search and review.

Linear algebra, multivariable calculus, vector fields, first and second-order ordinary differential equations, partial differential equations.

Software architecture, agile methods, design patterns, domain modeling and analysis (UML).

Databases and data architectures, real-world dataset analysis (Python, SQL, MapReduce) and visualization (matplotlib), individual research and report writing.

Web standards and protocols, distributed architectures (REST, SOAP), mobile frameworks (Java, Android), cloud infrastructure and operations (OpenStack).

Number theory, geometric transformations, differential equations, combinatorics, eigenvalues and eigenvectors, mathematical language and proof.

Introduction to programming with C++.

Search and sorting algorithms (Python), binary trees, mathematical notation (sets, functions, logic, and proof), computational complexity.

Software quality, team working, project and service management techniques (ITIL).

Client-server architecture, content markup (HTML, CSS, XML), usability and security (HTTPS), client and server side implementation languages (JavaScript, PHP, SQL).

Object-oriented concepts and principles with Java.

Computer architecture, networking, operating systems (Linux).

Trigonometric functions, vectors and matrices, differentiation and integration, sequences, complex numbers.

Spreadsheet techniques and best practices (PivotTables, VLOOKUPS, what-if analyses, graphs).

Introduction to data analysis with Python (Numpy, Matplotlib, Pandas).

Introduction to undergraduate-level computing and information technology.

Introduction to undergraduate-level business studies.

Introduction to undergraduate-level mathematics and statistics.

Introduction to programming with HTML, CSS, and Python.

Web development fundamentals, best practices, and professional problem-solving techniques using Developer Tools.

Introduction to Programming and Thinking in Computer Science

Introduction to programming concepts and ideas.

Introduction to programming with C.

Mathematics II (preparatory)

Introduction to undergraduate-level mathematics (in Swedish).

Spanish: Beginner Course I

Introduction to Spanish language and culture.

Physics I (preparatory)

Introduction to undergraduate-level physics.

Chemistry I (preparatory)

Introduction to undergraduate-level chemistry.

Introduction to evolutionary biology, ecology, and geology.

Mathematics I (preparatory)

Introduction to undergraduate-level mathematics (in Swedish).

Biology I (preparatory)

Introduction to undergraduate-level biology.