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	lecture_01.pdf lecture_01_notes.pdf
2	Sep, 30 (TB)	Classical methods and models in MLE with Scikit Learn	lecture_02.pdf lecture_02_notes.pdf lecture_02_scripts.zip
3	Oct, 07 (TB)	Deep learning engineering basics with PyTorch	lecture_03.pdf lecture_03_notes.pdf lecture_03_scripts.zip
4	Oct, 14 (JB)	ML System Design and Architecture	lecture_04.pdf 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	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:

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

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