Laboratory of Computer Science 1

Course Description

This course contains laboratory exercises of theoretical courses and some specialization courses, as well as contemporary skills with various programming and other tools. The laboratory assignments are grouped in three areas, designed to complement the material covered in lectures. The laboratory practice covers hands-on experience in: (A) specification, verification and design of advanced algorithms, (B) problem solving and illustrations of linear and nonlinear classifiers (neural networks, SVM, Bayes classifier, Application of KL transform and FLD for feature extraction, etc.).

General Competencies

In-depth understanding of principles and theoretical background complemented with practical implementation of advanced algorithms and data structures, operating system internals, machine learning and pattern recognition problems. Ability to design and conduct analytic, modeling, simulation and experimental investigation. Ability to design solutions to problems that are unfamiliar, incompletely defined, and have competing specifications. Ability to formulate the problem and critically evaluate the solution.

Learning Outcomes

  1. explain and define concepts of pattern recognition, advanced algorithms and data structures and machine learning
  2. explain and distinguish porocedures, methods and algorithms related to pattern recognition, advanced algorithms and data structures and machine learning
  3. apply methods from the pattern recognition, advanced algorithms and data structures and machine learning for new complex applications
  4. analyze and breakdown problem related to the complex system
  5. design and develop a system for the specific application
  6. evaluate quality of solution of a system

Forms of Teaching

Consultations

Consultations are held according to needs.

Seminars

Groups of 4 to 7 students receive project tasks. The group solves problem, implements the system and evaluates it.

Grading Method

     
Continuous Assessment Exam
Type Threshold Percent of Grade Threshold Percent of Grade
Seminar/Project 0 % 100 % 0 % 0 %
Exam: Oral 100 %

Week by Week Schedule

  1. Defining project assignments
  2. Work on project assignments
  3. Work on project assignments
  4. Work on project assignments. Consultations.
  5. 1st milestone
  6. Work on project assignments
  7. Work on project assignments
  8. Work on project assignments. Consultations.
  9. Work on project assignments
  10. 2nd milestone
  11. Work on project assignments
  12. Work on project assignments
  13. Work on project assignments
  14. Work on project assignments
  15. Defense of project and presenting the results

Study Programmes

University graduate
Computer Science (profile)
(1. semester)

Literature

R.O. Duda, P. E. Hart, D.G. Stork (2001.), Pattern Classification, J. Wiley, New York
M.A. Weiss (1996.), Data Structures and Algorithm Analysis in C, Addison Wesley
T. Mitchell (1997.), Machine Learning, McGraw-Hill Science/Engineering/Math

General

ID 35224
  Winter semester
5 ECTS
L1 English Level
L1 e-Learning
30 Lectures
0 Exercises
60 Laboratory exercises
0 Project laboratory

Grading System

89 Excellent
74 Very Good
61 Good
50 Acceptable