Degree | Type | Year | Semester |
---|---|---|---|
2503740 Computational Mathematics and Data Analytics | FB | 1 | 2 |
It is necessary that the student has taken Mathematics in the two baccalaureate courses and has examined this matter in the PAU.
[Translated from the Catalan version by Google translator]
Know the combinatorial graphs and their terminology
Know the different search algorithms and movement in graphs
Know the types of dynamic data for representation of graphs and their implemtation in C
Know the basic algorithms of optimal searches in graphs and their complexity
[Translated from the Catalan version by Google translator]
Combinatorial algorithms for graphics
Combined graphs and graph searches
Theory of graphs: introduction
Search in graphs: Depth-first and Breadth-first
Greedy Algorithms
Recursion
Abstract data types and object-oriented programming: Dynamic lists and trees
Graphic representation algorithms. Avantatgews disadvantages of each of the options.
Abstract and dynamic data types for graphs and their implementation in C.
Basic algorithms for optimal search graphs and their complexity
Calculation of distances from latitude and longitude
Algorithm of Dijkstra for optimal routes in graphs
Algorithm A *: heuristic search for optimal paths in graphs
[Translated from the Catalan version by Google translator]
The weekly sessions of the subject will be divided, usually, in two parts:
a) A theoretical part in which the teacher will introduce the concepts, methods and examples related to the syllabus of the subject.
b) A practical part in which students will be proposed a series of problems or exercises in which they will be shown and a concrete work will be worked on that which has been seen in the theoretical part. These sessions will be held in one of the faculty's computer rooms. Each practice will have a different statement, which will be published on the Virtual Campus and will involve the delivery of the answers to some questions raised. The delivery will be during the day the practice is performed.
Complementary exercises will also be proposed as an autonomous activity to help them understand the applied part of the subject.
[Translated from the Catalan version by Google translator]
Title | Hours | ECTS | Learning Outcomes |
---|---|---|---|
Type: Directed | |||
Attend the theoretical and practical classes | 56 | 2.24 | 2, 1, 10, 8, 9 |
Type: Supervised | |||
Completion of the practices | 55 | 2.2 | 2, 14, 6, 3, 1, 10, 12, 11, 8, 15, 9 |
Type: Autonomous | |||
Resolution of complementary exercises | 30 | 1.2 | 2, 14, 6, 3, 1, 10, 12, 11, 8, 15, 9 |
The evaluation will consist of the following activities:
A recoverable final exam, which accounts for 40% of the note
An individual practical work with a delivery period where it will be necessary to develop and implement algorithms, which account for 35% of the note. This is a continuous evaluation activity and is not recoverable
Delivery, during practical sessions, of practical exercises that will be carried out in groups of two. They count 25% of the note. This is a continuous evaluation activity and is not recoverable
The minimum grade in each of the three evaluation activities to be able to pass the subject is 3.5 points out of 10.
[Translated from the Catalan version by Google translator]
Title | Weighting | Hours | ECTS | Learning Outcomes |
---|---|---|---|---|
Delivery of practical exercices | 25% | 0 | 0 | 2, 14, 4, 5, 1, 10, 13, 12, 11, 8, 15, 9 |
End of term exam | 40% | 4 | 0.16 | 4, 1, 7, 10, 11, 8 |
Individual practical work | 35% | 5 | 0.2 | 2, 14, 6, 3, 4, 5, 1, 10, 12, 11, 8 |