Advanced Algorithms and Data Structures 510-IS2-2ZASD-23-ENG
Profile of studies: general academic
Form of studies: full-time
Course type: obligatory
Field and discipline of science: exact and natural sciences, computer science
Year of studies/semester: 2 / 3
Preliminary requirements (so-called sequential system of classes and examinations): no
Number of class hours:
- lecture: 30 h
- laboratory classes: 15 h
Didactic methods: lecture, presentation, discussion, laboratory, project.
ECTS credits: 4
Details of student's workload (activity and number of hours):
- Attendance at classes:
- - lecture : 30 h
- - laboratory: 15 h
- Preparation for classes:
- - laboratory: 3 h
- Literature studying: 15 h
- Student's own works on computer programs, projects, reports, presentations, homeworks: 15 h
- Preparation for exam: 8 h
- Attendance at exam: 2 h
- Attendance at office hours: 12 h
Quantitative indicators:
- Student's workload related to activities that require direct participation of a teacher: 59 h / 2,4 ECTS
- Student's workload related to activities that do not require direct participation of a teacher: 41 h / 1,6 ECTS
Type of course
Mode
(in Polish) zdalnie
Course coordinators
Learning outcomes
- Has got enhanced knowledge of algorithms, data structures, and methods for implementing them. KP7_WG1
- Knows techniques for solving difficult algorithmic problems. KP7_WG1
- Is able to develop a solution to a nontrivial algorithmic problem and to study the solution with respect to correctness and complexity. KP7_UO4, KP7_UK4, KP7_UW3
- Is able to implement a complicated algorithm as a computer program, optimizing computations and memory usage. KP7_UO4, KP7_UK4, KP7_UW3
- Is able to retrieve, evaluate, and integrate informations related to a problem, so as to develop an optimal algorithmic solution. KP7_UU1
- Understands the necessity for continuously improving his/her skills. KP7_UU2
Assessment criteria
Credit type: examination
Students cannot be examined before passing the laboratory classes.
Rules of AI usage v.25.09.29
Students can use AI systems only to the extent permitted by law acts adopted by the University and Faculty, to automatize mechanic actions that require nether creative nor critical thinking, and that do not require understanding processes and technologies.
Students are prohibited from using AI to do tasks that aim to develop students' creativity, skills and knowledge, that should be solved without automatization in order to ensure the learning outcomes.
Questions and doubts about AI usage should be communicated to the teacher.
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: