Study programme 2017-2018 | Français | ||
Selected Topics in Computer Science | |||
Programme component of Master's Degree in Computer Engineering and Management à la Faculty of Engineering |
Code | Type | Head of UE | Department’s contact details | Teacher(s) |
---|---|---|---|---|
UI-M2-IRIGIG-204-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 | 30 | 0 | 30 | 0 | 0 | 5 | 5 | 2nd term |
AA Code | Teaching Activity (AA) | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Term | Weighting |
---|---|---|---|---|---|---|---|---|
I-MARO-220 | Advanced Data Science and Machine Learning | 30 | 0 | 30 | 0 | 0 | Q2 | 100.00% |
Programme component |
---|
Objectives of Programme's Learning Outcomes
Learning Outcomes of UE
- have a global overview of the cutting-edge methods in data science
- understand and explain the theory, models and techniques used
- identify which model(s) are best suited for a given dataset
- analyse datasets using a software
- interpret the results from the software, showing an understanding of the theory
Content of UE
deep networks ; reinforcement learning ; active learning ; ensemble methods (random forests, ...) ; statistical learning theory
Prior Experience
Not applicable
Type of Assessment for UE in Q2
Q2 UE Assessment Comments
Written exam for the theory, and practical problem sets on the computer, with equal weights.
Type of Assessment for UE in Q3
Q3 UE Assessment Comments
Written exam for the theory, and practical problem sets on the computer, with equal weights.
Type of Teaching Activity/Activities
AA | Type of Teaching Activity/Activities |
---|---|
I-MARO-220 |
|
Mode of delivery
AA | Mode of delivery |
---|---|
I-MARO-220 |
|
Required Reading
AA | |
---|---|
I-MARO-220 |
Required Learning Resources/Tools
AA | Required Learning Resources/Tools |
---|---|
I-MARO-220 | - slides of oral presentations (theory and examples) - problem sets |
Recommended Reading
AA | |
---|---|
I-MARO-220 |
Recommended Learning Resources/Tools
AA | Recommended Learning Resources/Tools |
---|---|
I-MARO-220 | Not applicable |
Other Recommended Reading
AA | Other Recommended Reading |
---|---|
I-MARO-220 | - Abu-Mostafa, Yaser S., Malik Magdon-Ismail, and Hsuan-Tien Lin. Learning from data. (2012) - Mitchell, Tom M. Machine learning (1997). - Christopher M. Bishop, Pattern Recognition and Machine Learning (2012) |
Grade Deferrals of AAs from one year to the next
AA | Grade Deferrals of AAs from one year to the next |
---|---|
I-MARO-220 | Authorized |