This course on Algorithms allows the computer science engineering student to delve into algorithm design techniques, taking into account qualitative and quantitative factors in their evaluation. On the one hand, it completes the training on the writing of efficient and correctly structured programs. On the other hand, it approaches the most common problem-solving techniques that an engineer can find.
It is worth noting that the conduction of experiments involving runtime measurements on different algorithms provides an empirical approach that is usually highly regarded by the student, who can thus establish the concrete interpretation of the complexities found. The difficulties that arise in some of the studied cases allow for a complementary reflection on aspects like computing resource management, process execution details, architectures and operating systems used, etc.
The study and analysis of an important set of fundamental algorithms is also worth remarking, covering a large range of algorithmic techniques and their applications. The possibility of using different techniques for the resolution of some problems results naturally into thinking about the advantages and disadvantages of the different strategies, and the need to know how to choose the best alternative for each particular scenario.
Lastly, it is important to develop the necessary rigor to develop solutions that not only adapt to a given specification, but also do so in an efficient way from the viewpoint of the needed computational resources. This will be illustrated by means of various practical cases where the existence of known efficient algorithms leads us to reject alternative designs, even when they look very natural at a first glance.
Contingency plan
1. Modifications to the contents
No changes.
2. Methodologies
*Teaching methodologies that are maintained
Tutored works
*Teaching methodologies that are modified
Lecture: It will be done remotely, through Teams and/or recorded videos.
Short answer test: It will be done remotely, through Teams and Moodle.
Laboratory practicals: They will be carried out remotely, using Teams. A laptop configuration with linux, gcc and svn is recommended.
Problem solving: It will be done remotely, using Teams and Moodle.
Objective test: It will be done remotely, using Teams and Moodle, in addition to the recommended configuration of the laptop with linux, gcc and svn for the practical part.
3. Mechanisms for personalized attention to students
Email: daily, for inquiries.
Moodle: daily, to access class materials, check the calendar or use the forums.
Teams: during the theory, SGT or practical hours foreseen in the course schedule; group tutorials on the theory (2h per week) and on the practicals (2h per week); regarding individual tutoring, it will be maintained during each teacher's office hours upon request via email.
4. Modifications in the evaluation
All tests (continuous assessment and objective test) are made remotely.
*Evaluation observations:
The provisions of the teaching guide are maintained.
An alternative date will be offered to students who have logistical problems at the time of the tests.
5. Modifications to the bibliography or webgraphy
No changes.
(*)The teaching guide is the document in which the URV publishes the information about all its courses. It is a public document and cannot be modified. Only in exceptional cases can it be revised by the competent agent or duly revised so that it is in line with current legislation.
Subjects
