CourseSpecifications
Valid as from the academic year 2024-2025

Machine Learning (E061330)

Course size
Credits 6.0(nominal values; actual values may depend on programme)
Study time 180 h

Course offerings and teaching methods in academic year 2024-2025
A (semester 1)
Dutch
Gent

B (semester 1)

Englishgroup work
lectureGent

Lecturers in academic year 2024-2025
Dambre, Joni
Dhaene, Tom

30.0h
30.0h

independent work
lectureTW06TW05

lecturer-in-charge
co-lecturer

Offered in the following programmes in 2024-2025
crdts
Bridging Programme Master of Science in Bioinformatics(main subject Engineering)
6Bridging Programme Master of Science in Computer Science Engineering
6
Master of Science in Electrical Engineering (main subject Communication and Information 6Technology )Master of Science in Bioinformatics(main subject Engineering)
6
Master of Science in Industrial Engineering and Operations Research(main subject
6
Manufacturing and Supply Chain Engineering)
Master of Science in Industrial Engineering and Operations Research(main subject
6Transport and Mobility Engineering)
European Master of Science in Nuclear Fusion and Engineering Physics
6
Master of Science in Biomedical Engineering
6
Master of Science in Biomedical Engineering
6
Master of Science in Computer Science Engineering
6
Master of Science in Computer Science Engineering
6
Master of Science in Industrial Engineering and Operations Research
6
Master of Science in Photonics Engineering
6Exchange Programme in Bioinformatics (master's level)
6offering
B
B
B
B
B
B
B
B
B
A
B
A, B
B
B

Teaching languages
English, Dutch
Keywords
Machine learning, deep learning, probabilistic and Bayesian models, Gaussian processes,
design and analysis of AI systems, AI and society
Position of the course
The objective of this course is to provide theoretical and practical insights into the use ofcontemporary machine learning in advanced practical applications. It also offers a broad
perspective on recent evolutions in the field and a critical analysis of their impact on people andsociety. A theoretical framework is offered better understand the benefits and limitations ofeach technique, but the focus is on understanding how to correctly apply machine learning inpractice. This course comes with a hands-on component, in which you learn to apply
techniques, design multi-component AI-systems and learn to analyse their performance.
For the application of machine learning models, the Python programming language is used.
Students are therefore expected to be fluent in Python programming and have previous(Approved)1experience with the Machine learning library Scikit Learn.
Contents• Brief historical overview and revision of the most important fundamental concepts of machine• learning• Advanced unsupervised learning techniques for clustering, dimensionality reduction and• density estimation• Deep learning: theory and practical network design, transfer learning, deep unsupervised and• self-supervised learning, attention and transformers, introduction to recent technologies for• large language models and image generation• Advanced probabilistic and Bayesian techniques, Gaussian processes, variational learning• Risks and pitfalls of powerful deep learning: algorithmic bias, overconfidence, (lack of)• explainability, unfairness (and techniques to address them)• Impact of recent evolutions in AI on society, ethical and legal considerations
Initial competences
Mathematics: calculus, linear algebra, analytic geometry, probability theory and statistics (allat university level).
Theoretical and mathematics-based basic knowledge on Machine learning, as provided
by UGent's Computer Science engineering bachelor course " Artificial Intelligence E016350 (orthe 3-credit B-session adopted in other engineering bachelors), including basic experience
with the machine learning library Scikit Learn.
A good practical proficiency of Python programming: fluent use of common libraries such asnumpy or pandas.
Final competences
1 Understand the fundamental principles and challenges of machine learning.
2 Understand the mathematical background of some common and advanced machine learning
1 models.
3 Implement simple machine learning models and correctly apply machine learning libraries for
1 more advanced techniques.
4 Analyse a new machine learning problem and address it by correctly applying the principles
1 of machine learning and selecting suitable common machine learning models.
5 Understand and critically evaluate the techniques presented in scientific literature on
1 machine learning.
Conditions for credit contract
Access to this course unit via a credit contract is determined after successful competences assessment
Conditions for exam contract
This course unit cannot be taken via an exam contract
Teaching methods
Group work, Lecture, Independent work
Extra information on the teaching methods
The lectures take place on-campus. Recordings Are made available through Ufora after eachlecture. In-class discussions are not recorded. Livestreaming is not available.
The NPE will consist of a number of hands-on assignments which will have to be madeindividually or in groups. Coaching is provided in-person on-campus during hands-on lecture
hours or online (through MsTeams). In-depth feedback is provided collectively during lectures.
Study material
Type: Slides
Name: Lecture slides
Indicative price: Free or paid by faculty
Optional: noLanguage : English
Available on Ufora : Yes
Type: Audiovisual Material
Name: Lecture recordings
Indicative price: Free or paid by faculty
Optional: no
Language : English
Available on Ufora : Yes(Approved)

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Type: Other
Name: Jupyter notebooks and code examples used in lectures and hands-on assignments
Indicative price: Free or paid by faculty
Optional: noLanguage : English
Available on Ufora : Yes
References
References to recent and online available sources will be given during lectures and madeavailable through Ufora if necessary.
Course content-related study coaching
By the teachers and the assistants, before, during or after contact sessions, by appointment orthrough MsTeams chat.
Assessment moments
end-of-term and continuous assessment
Examination methods in case of periodic assessment during the first examination period
Written assessment
Examination methods in case of periodic assessment during the second examination period
Written assessment
Examination methods in case of permanent assessment
Skills test, Participation, Assignment
Possibilities of retake in case of permanent assessment
examination during the second examination period is possible in modified form
Extra information on the examination methods
The exam consists of a written examination (closed book). It consists of several short questions
to test knowledge and understanding (multiple choice or open questions with very shortanswers), as well as a few questions that evaluate deeper understanding.
The NPE for this course consists of multiple graded assignments (individual or in groups) inwhich students apply the principles from the theory lectures and deepen their understanding.
The second exam period only consists of individually graded work.
Calculation of the examination markThe final score for the course is a weighted average, consisting of 35% NPE (evaluation during
the semester), and 65% PE (exam).
Participation to the NPE assignments is mandatory in order to succeed.
You need to obtain a score of at least 9/20 on each of both parts (NPE and PE) in order toobtain a credit. Students who do not fulfill the second condition but for whom the calculated
score would be 9/20 or more, will receive a truncated score of 8/20 (i.e., the largest integerscore that is smaller than 9/20).
Resit:
If you failed for the course in first session, the ratio between practical work and exam remains
the same in resit. If you passed for the exam but not for the course, you do not have to retakethe exam. If you passed for NPE but not for the course, you only have to retake the exam.
The resit exam covers the same materials as the first session exam.
Since a resit assignment for NPE can never cover all aspects of the practical work during thesemester, you can only obtain an NPE score correction for NPE in resit. You will receive anindividual assignment to assess your practical skills. This will allow you to correct your NPE
score of the first session: your final score for NPE will be calculated as 0.6*(score NPE firstsession)+0.4*(score NPE resit).
Overall, you still need to obtain a score of at least 9/20 on each of both parts (NPE and PE) inorder to obtain a credit. Students who do not fulfill the second condition but for whom thecalculated score would be 9/20 or more, will receive a truncated score of 8/20 (i.e., the largest
integer score that is smaller than 9/20).
Facilities for Working Students
Lecture recordings are made available after each theory lecture. Attendance for most of thehands-on lecture blocks is optional. The planning of hands-on assignments is announced at thestart of the term and enough time is provided to allow planning for the assignments themselves.
Only in very exceptional cases, a deadline extension can be allowed. In this case the extension(Approved)

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must be requested and well motivated as soon as possible and before the start of theassignment. A deadline can never be postponed beyond the lecture in which collective
feedback on the assignment is given.

(Approved)

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