====== BECM33MLE Machine Learning Engineering ======
===== The focus =====
The aim of this course is to provide practical knowledge about how to apply Machine Learning methods in real world settings. The course features lectures given by industry experts from leading companies in the field. The practicals will provide hands-on experience with how to design a product/service featuring ML pipeline.
==== Lectures: Tuesday, 09:15-10:45, KN:A-404 ====
Lecturers: Tomáš Báča (TB), Jan Brabec (JB), Jan Lukány (JL)
^ Week ^ Date ^ Topic ^ Materials ^
| 1 | Sep, 23 (TB) | Introduction to Machine Lecture Engineering | {{ :courses:becm33mle:lectures:lecture_01.pdf |}} {{ :courses:becm33mle:lectures:lecture_01_notes.pdf |}} |
| 2 | Sep, 30 (TB) | Classical methods and models in MLE with Scikit Learn | {{ :courses:becm33mle:lectures:lecture_02.pdf |}} {{ :courses:becm33mle:lectures:lecture_02_notes.pdf |}} {{ :courses:becm33mle:lectures:lecture_02_scripts.zip |}} |
| 3 | Oct, 07 (TB) | Deep learning engineering basics with PyTorch | {{ :courses:becm33mle:lectures:lecture_03.pdf |}} {{ :courses:becm33mle:lectures:lecture_03_notes.pdf |}} {{ :courses:becm33mle:lectures:lecture_03_scripts.zip |}} |
| 4 | Oct, 14 (JB) | ML System Design and Architecture | {{ :courses:becm33mle:lectures:lecture_04.pdf |}} {{ :courses:becm33mle:lectures:lecture_04_notes.pdf |}} |
| 5 | Oct, 21 (JL) | Data storage frameworks | |
| - | Oct, 28 (-) | Canceled - National holiday | |
| 6 | Nov, 04 (JL) | Machine learning model execution paradigms | |
| 7 | Nov, 11 (JB+TB) | Ground truth management | |
| 8 | Nov, 18 (JB) | Production metrics and observability | |
| 9 | Nov, 25 (JB) | ML and AI technical debt | |
| 10 | Dec, 02 (TB) | AI engineering, MCP | |
| 11 | Dec, 09 (TB) | Containerization (Docker, Apptainer) | |
| 12 | Dec, 16 (TB) | Development workflows (git, CI-CD, BDD) | |
| 13 | Jan, 06 (TB) | MLE and AI on the "edge" | |
===== Labs: Wednesday =====
* labs leader: Ing. David Pařil (parildav@fel.cvut.cz)
* labs attendance: compulsory (max absences 3; each extra absence -5 points)
Hands-on design, develop, deploy and present a small application/service/product that uses Machine Learning in its core.
==== Labs: Tuesday, 11:00-12:30, KN:A-420 ====
^ Week ^ Date ^ Topic ^ Deadlines ^ Materials ^
| 1 | Sep, 23 | Introduction | HW 01, PRD proposal | {{ courses:becm33mle:labs:lab01_introduction.pdf |}} |
| 2 | Sep, 30 | GUI + Project setup | HW 02, Readme | |
| 3 | Oct, 07 | Datasets | HW 03, Dataset | |
| 4 | Oct, 14 | Reinforcement learning in a virtual environment | | |
| 5 | Oct, 21 | Machine learning basics | HW 04, 1st progress report | |
| - | Oct, 28 | Canceled - National holiday | | |
| 6 | Nov, 04 | Implementation details | | |
| 7 | Nov, 11 | Machine learning advanced | | |
| 8 | Nov, 18 | Deployment | | |
| 9 | Nov, 25 | Going to market | HW 05, 2nd progress report | |
| 10 | Dec, 02 | Workshop | | |
| 11 | Dec, 09 | Workshop | | |
| 12 | Dec, 16 | Workshop | | |
| 13 | Jan, 06 | Project presentations | HW 06, Final report | |
==== Semestral Project ====
Lab 1 link **TODO**
==== Final evaluation ====
The total amount of points is the summation of
* The points for the project (up to 50 points),
* The points for homeworks (up to 15 points),
* The points for documentation (up to 15 points),
* The points for the project presentation (up to 20 points),
^ Points ^ [0,50) ^ [50,60) ^ [60,70) ^ [70,80) ^ [80,90) ^ [90,100] ^
| Mark | F | E | D | C | B | A |
==== Late submissions ====
Late submissions will be penalized by -2 points penalty.
===== Contacts =====
Lectures:
* [[http://mrs.felk.cvut.cz/people/tomas-baca|Ing. Tomas Baca, Ph.D.]], tomas.baca@fel.cvut.cz (Guarantee)
* Ing. Jan Brabec, Ph.D.
* Ing. Jan Lukany
Labs:
* Ing. David Pařil, parildav@fel.cvut.cz