 | Study programme 2022-2023 | Français | |
 | Machine Learning II | ||
Programme component of Master's in Computer Science (MONS) (day schedule) à la Faculty of Science |
| Code | Type | Head of UE | Department’s contact details | Teacher(s) |
|---|---|---|---|---|
| US-M1-SCINFO-058-M | Optional UE | BEN TAIEB Souhaib | S861 - Big Data and Machine Learning |
|
| Language of instruction | Language of assessment | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Credits | Weighting | Term |
|---|---|---|---|---|---|---|---|---|---|
| Anglais | 30 | 30 | 0 | 0 | 0 | 6 | 6.00 | 2nd term |
| AA Code | Teaching Activity (AA) | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Term | Weighting |
|---|---|---|---|---|---|---|---|---|
| S-INFO-075 | Machine Learning II | 30 | 30 | 0 | 0 | 0 | Q2 | 100.00% |
| Programme component |
|---|
Objectives of Programme's Learning Outcomes
- Manage research, development and innovation.
- Understand unprecedented problems in computer science and its applications.
- Master communication techniques.
- Communicate, both orally and in writing, their findings, original proposals, knowledge and underlying principles, in a clear, structured and justified manner.
- Develop and integrate a high degree of autonomy.
- Aquire new knowledge independently.
- Apply scientific methodology.
- Critically reflect on the impact of IT in general, and on the contribution to projects.
Learning Outcomes of UE
See single learning activity.
UE Content: description and pedagogical relevance
This unit is a follow-up to the first course in machine learning. It aims to supplement and deepen knowledge in machine learning and data analysis.
Prior Experience
"Machine learning I" is a pre-requisite for this unit.
Type of Teaching Activity/Activities
| AA | Type of Teaching Activity/Activities |
|---|---|
| S-INFO-075 |
|
Mode of delivery
| AA | Mode of delivery |
|---|---|
| S-INFO-075 |
|
Required Learning Resources/Tools
| AA | Required Learning Resources/Tools |
|---|---|
| S-INFO-075 | Not applicable |
Recommended Learning Resources/Tools
| AA | Recommended Learning Resources/Tools |
|---|---|
| S-INFO-075 | Not applicable |
Other Recommended Reading
| AA | Other Recommended Reading |
|---|---|
| S-INFO-075 | Not applicable |
Grade Deferrals of AAs from one year to the next
| AA | Grade Deferrals of AAs from one year to the next |
|---|---|
| S-INFO-075 | Unauthorized |
Term 2 Assessment - type
| AA | Type(s) and mode(s) of Q2 assessment |
|---|---|
| S-INFO-075 |
|
Term 2 Assessment - comments
| AA | Term 2 Assessment - comments |
|---|---|
| S-INFO-075 | Closed-book oral exam (60% of total score) Project (20% of total score) Assignments (20% of total score) There is a hurdle of 50% for each of the previous evaluations |
Term 3 Assessment - type
| AA | Type(s) and mode(s) of Q3 assessment |
|---|---|
| S-INFO-075 |
|
Term 3 Assessment - comments
| AA | Term 3 Assessment - comments |
|---|---|
| S-INFO-075 | Closed-book oral exam (60% of total score) Project (20% of total score) Assignments (20% of total score) There is a hurdle of 50% for each of the previous evaluations |
(*) HT : Hours of theory - HTPE : Hours of in-class exercices - HTPS : hours of practical work - HD : HMiscellaneous time - HR : Hours of remedial classes. - Per. (Period), Y=Year, Q1=1st term et Q2=2nd term