Probability, Statistics and Optimization (5 cr)
Code: R504D95-3001
General information
Enrollment
03.10.2022 - 15.01.2023
Timing
16.01.2023 - 30.04.2023
Credits
5 op
Mode of delivery
Contact teaching
Unit
Bachelor of Engineering, Information Technology
Teaching languages
- English
Seats
0 - 30
Degree programmes
- Machine Learning and Data Engineering
Teachers
- Jyri Kivinen
Responsible person
Jyri Kivinen
Student groups
-
R54D22S
Objective
The student learns fundamental mathematical concepts, principles, tools (including computing environments) and terminology of statistics, probability and optimization for professional studies.
Content
Theory of statistics and probability
- Numerical and graphical description of data
- Probability, probability rules and theorems, probability distribution
- Modeling, parameter estimation
- Model selection, decision theory
- Analysis task types
Optimization, differential calculus and numerical computation
- Objective function
- Critical points, extrema
- Types of optimization problems
- Limit, derivative, partial derivative, differentiation rules
- Iterative gradient-based optimization methods, derivative-free methods
Location and time
Spring 2023, Rantavitikka (Jokiväylä 11, Rovaniemi).
Materials
Materials shall be put to Moodle workspace for the course unit.
Teaching methods
Lectures, exercises.
Exam schedules
Examination dates shall be decided in the beginning of the course unit.
Content scheduling
Teaching throughout the spring semester, roughly similar amount of teaching/week (4 hrs/week).
Evaluation scale
H-5
Assessment criteria, satisfactory (1)
Assessment criteria - grade 1
The student knows the concepts of probability, statistics, and optimization and is able to solve basic problems.
Assessment criteria, good (3)
Assessment criteria - grade 3
The student understands the concepts of probability, statistics, and optimization and is able to solve varied problems related to applications of probability, statistics, and optimization.
Assessment criteria, excellent (5)
The student understands the concepts of probability, statistics, and optimization and is able to apply methods of probability, statistics, and optimization in solving and handling new types of problems.
Assessment methods and criteria
Examination, in-class presence.
Assessment criteria, satisfactory (1-2)
Grade 1:
The student knows the concepts of probability, statistics, and optimization and is able to solve basic problems.
Assessment criteria, good (3-4)
Grade 3:
The student understands the concepts of probability, statistics, and optimization and is able to solve varied problems related to applications of probability, statistics, and optimization.
Assessment criteria, excellent (5)
The student understands the concepts of probability, statistics, and optimization and is able to apply methods of probability, statistics, and optimization in solving and handling new types of problems