 | Study programme 2021-2022 | Français | |
 | Data Science for Artificial Intelligence | ||
Programme component of Bachelor's in Engineering à la Faculty of Engineering |
| Code | Type | Head of UE | Department’s contact details | Teacher(s) |
|---|---|---|---|---|
| UI-B3-IRCIVI-313-M | Compulsory UE | SIEBERT Xavier | F151 - Mathématique et Recherche opérationnelle |
|
| Language of instruction | Language of assessment | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Credits | Weighting | Term |
|---|---|---|---|---|---|---|---|---|---|
| Anglais, Français | 34 | 50 | 0 | 0 | 0 | 7 | 7.00 | 1st term |
| AA Code | Teaching Activity (AA) | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Term | Weighting |
|---|---|---|---|---|---|---|---|---|
| I-ISIA-030 | Signal Processing 1 | 16 | 32 | 0 | 0 | 0 | Q1 | 57.00% |
| I-MARO-014 | Data Mining | 18 | 18 | 0 | 0 | 0 | Q1 | 43.00% |
| Programme component | ||
|---|---|---|
 | UI-B1-IRCIVI-003-M Engineering Mathematics 1 | |
| UI-B2-IRCIVI-002-M Applied Mathematics | |
| UI-B2-IRCIVI-003-M Probability and Statistics | |
| UI-B2-IRCIVI-009-M Fields, Signals and Systems | |
Objectives of Programme's Learning Outcomes
- Implement an engineering approach dealing with a set problem taking into account technical, economic and environmental constraints
- Identify and describe the problem to be solved and the functional need (of prospective clients) to be met considering the state of technology
- Identify and acquire the information and skills needed to solve the problem
- Understand the theoretical and methodological fundamentals in science and engineering to solve problems involving these disciplines
- Identify, describe and explain basic scientific and mathematical principles
- Identify, describe and explain the basic principles of engineering particularly in their specialising field
- Select and rigorously apply knowledge, tools and methods in sciences and engineering to solve problems involving these disciplines
- Communicate in a structured way - both orally and in writing, in French and English - giving clear, accurate, reasoned information
- Present analysis or experiment results in laboratory reports
- Demonstrate thoroughness and independence throughout their studies
- Identify the different fields and participants in engineering
- Develop their scientific curiosity and open-mindedness
- Learn to use various resources made available to inform and train independently
Learning Outcomes of UE
- analyze various kinds of data and signals - understand the underlying theory for the development of basic components of a numerical signal processing system - implement these components with MATLAB - understand and explain the theory, models and techniques used for statistical data analysis - analyse datasets with a given software (Python, MATLAB, R, Weka, ...) - interpret the results from the software, showing an understanding of the theory
Content of UE
- linear and invariant numerical systems; frequency analysis of numerical signals and systems; Shannon theorem and sampling; discrete Fourier transform; spectral analysis of random signals; numerical filters; simple systems under MATLAB - descriptive techniques for data analysis (principal components analysis, discriminant factorial analysis) - classical models of data analysis (analysis of variance, linear regression) - data mining / machine learning (classification and clustering)
Prior Experience
Algebra, Analysis, probability and statistics, functions of complex variables
Type of Assessment for UE in Q1
- Presentation and/or works
- Written examination
- Practical test
Q1 UE Assessment Comments
Weights : Signal processing : 4/7 Statistical Data Analysis : 3/7
Type of Assessment for UE in Q3
- Written examination
Q3 UE Assessment Comments
idem Q1
Type of Resit Assessment for UE in Q1 (BAB1)
- N/A
Q1 UE Resit Assessment Comments (BAB1)
n/a
Type of Teaching Activity/Activities
| AA | Type of Teaching Activity/Activities |
|---|---|
| I-ISIA-030 |
|
| I-MARO-014 |
|
Mode of delivery
| AA | Mode of delivery |
|---|---|
| I-ISIA-030 |
|
| I-MARO-014 |
|
Required Reading
| AA | Required Reading |
|---|---|
| I-ISIA-030 | Note de cours - Traitement du Signal (1 et 2) - Thierry Dutoit |
| I-MARO-014 |
Required Learning Resources/Tools
| AA | Required Learning Resources/Tools |
|---|---|
| I-ISIA-030 | Not applicable |
| I-MARO-014 | - slides of oral presentations (theory and examples) - problem sets |
Recommended Reading
| AA | |
|---|---|
| I-ISIA-030 | |
| I-MARO-014 |
Recommended Learning Resources/Tools
| AA | Recommended Learning Resources/Tools |
|---|---|
| I-ISIA-030 | Not applicable |
| I-MARO-014 | Sans objet |
Other Recommended Reading
| AA | Other Recommended Reading |
|---|---|
| I-ISIA-030 | AUGER, F. (1999) Introduction à la théorie du signal et de l'information, 461 pp. Paris : TechnipDENBIGH, P. (1998) System Analysis and Signal Processing, 513 pp. Harlow : Addison-WesleyBAHER, H. (2001) Analog and Digital Signal Processing, 497 pp. Chichester : Wiley & SonsLYONS, R.G. (1998) Understanding Digital Signal Processing, 517pp. Harlow : Addison-Wesley |
| I-MARO-014 | R.O.Duda, P.E.Hart, D.G.Stork. "Pattern Classification". John Wiley and Sons, 2000. C.M. Bishop Pattern recognition and machine learning. springer, 2006. R.E.Walpole, R.H.Myers, S.L.Myers, K.Ye, "Probability and Statistics for Engineers and Scientists", Prentice Hall, 2012 K P Murphy, Machine learning: a probabilistic perspective. MIT press, 2012. |
Grade Deferrals of AAs from one year to the next
| AA | Grade Deferrals of AAs from one year to the next |
|---|---|
| I-ISIA-030 | Authorized |
| I-MARO-014 | Authorized |