Learning objectives
The course provides a hands-on practice about algorithms and data structures. It guides in the implementation of such concepts
Taking Dublin Indicators into account:
Knowledge and understanding
The course recaps the contents of "algorithms and data structures" class and provides the student a means to comprehend such aspects. The design and practical implementation develops a deep confidence in the usage of such algorithms.
The reference text is in Italian/Engligh. Standard English terminology is commonly used during the lessons as goodwill to the consultation of the international scientific literature.
Applying knowledge and understanding
The knowledge presented is always applied to the resolution of specific problems. The companion exercises are focused on problem solving and testing the comprehension of proposed algorithms. Solution methods are presented in the form of an algorithm, providing the students the ability to formalize and adapt known algorithms to specific contexts.
Making judgments
The exercises, which are proposed in relation to the theoretical part presented in class, can be solved individually or in small groups. The comparison with classmates, work at home or in classroom, favors the development of specific skills in students to enable the explanation of arguments to fellows and teachers. Often the exercises can be solved in many different ways and listening to the solutions proposed by other allows students to develop the ability to identify common structures, beyond the apparent superficial differences.
Communication skills
The numerous discussions on the different methods to solve problems allow students to improve communication skills. Specific communication of computer technology is also used during classes and exercises.
Learning skills
The explanation of algorithms and data structures and their implementation in a high level language (non-object oriented) offers the chance to focus on the core concepts of algorithms: the degsign of a data structure, the deep knowledge of classical algorithms, the design of customized algorithms, the comprehension of computational complexity.
The knowledge acquired is never rigid and definitive, but it is adaptable to any evolution and change of perspective and context.