Study programme 2022-2023 | Français | ||
Data Science for Artificial Intelligence | |||
Programme component of Bachelor's in Engineering (MONS) (day schedule) 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-B2-IRCIVI-002-M Applied Mathematics | ||
UI-B2-IRCIVI-003-M Probability and Statistics |
Objectives of Programme's Learning Outcomes
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
UE Content: description and pedagogical relevance
- 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 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 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 |
Term 1 Assessment - type
AA | Type(s) and mode(s) of Q1 assessment |
---|---|
I-ISIA-030 |
|
I-MARO-014 |
|
Term 1 Assessment - comments
AA | Term 1 Assessment - comments |
---|---|
I-ISIA-030 | Project report, written, 2 hours, 35% Written exam, on exercises (no theory), 3 hours, 65% |
I-MARO-014 | written exam for the theory, practial work on the computer |
Resit Assessment - Term 1 (B1BA1) - type
AA | Type(s) and mode(s) of Q1 resit assessment (BAB1) |
---|---|
I-ISIA-030 |
|
I-MARO-014 |
|
Term 3 Assessment - type
AA | Type(s) and mode(s) of Q3 assessment |
---|---|
I-ISIA-030 |
|
I-MARO-014 |
|
Term 3 Assessment - comments
AA | Term 3 Assessment - comments |
---|---|
I-ISIA-030 | Written exam on exercises (no theory), 3 hours, 100% |
I-MARO-014 | idem Q1 |