Duration
15h Th, 10h Pr, 25h Proj.
Number of credits
| Bachelor of Science (BSc) in Engineering | 3 crédits | |||
| Bachelor of Science (BSc) in Computer Science | 3 crédits |
Lecturer
Language(s) of instruction
French language
Organisation and examination
Teaching in the first semester, review in January
Schedule
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
The course provides an introduction to the mathematical theory behind statistical methods and theoretical guarantees for the statistical methods that you may use for certain applications of engineering and science.
The following topics are addressed:
- Models, likelihood, estimation, and method of moments;
- Loss Functions, bias-variance tradeoff, and asymptotics;
- Maximum likelihood estimation;
- Confidence interval;
- Regression;
- Hypothesis testing;
- Bayesian statistical inference.
Learning outcomes of the learning unit
At the end of the course, the student will understand the fundamental principles of statistics, and he will be able to apply them to carry out exploratory data analyses, population parameter estimation, and hypothesis testing.
Prerequisite knowledge and skills
Calculus, algebra, geometry and probability. Elements of computer science and applied mathematics.
Planned learning activities and teaching methods
The course is composed of about 12 hours of theoretical lectures, 10 hours of classroom exercises, and 25 hours of assistance to the realization of practical projects with the computer.
Mode of delivery (face to face, distance learning, hybrid learning)
Face-to-face course
Course materials and recommended or required readings
Further information:
The course material will be made available as the semester progresses.
Additional references:
- Rice, John A. Mathematical Statistics and Data Analysis. New Delhi: Cengage Learning/Brooks/Cole, 2014.
- Wasserman, Larry. All of Statistics: A Concise Course in Statistical Inference. Corrected second printing, 2005. Springer Texts in Statistics. New York, NY: Springer, 2010.
Exam(s) in session
Any session
- In-person
written exam
Written work / report
Additional information:
The assessment is composed of two grades: a grade for the projects (approximately 15% of the final grade) and a grade for the written exam covering theory and exercises (approximately 85% of the final grade).
The projects will be subject to a single evaluation during the year and the grade obtained during the year will be used in determining the average grade for the first session and the second session (if applicable).
Work placement(s)
Organisational remarks and main changes to the course
Contacts
Lecturer: Pierre Sacré (p.sacre@uliege.be). Webpage: https://people.montefiore.uliege.be/sacre/MATH0487/.