Course concept

English-taught semesters, what are they?

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An English-taught semester is a coherent package of course units which a Howest Bachelor programme offers in English, as an option for semester exchange , to incoming students from its partner universities abroad, and as a study abroad option to other international students.

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Course overview

AI Engineer FALL

AI Engineer 30 ECTS
Machine Learning  (crash course) 6 ECTS
Deep Learning 6 ECTS
AI Engineer Essentials 6 ECTS
Basic Dutch
Meeting Flanders Today

(Optional modules: both can be taken to replace MLOps)
3 ECTS
3 ECTS
 
MLOps (optional course) 6 ECTS
AI For Healthcare 6 ECTS
Research Project  6 ECTS

AI Engineer SPRING

AI Engineer 30 ECTS
Advanced AI 6 ECTS
Internship 24 ECTS

Only available to the students who also applied for AI Engineer fall. Cannot be followed separately.

Course Unit Descriptions for All English-taught semester programmes
(if not available yet for the upcoming academic year please select the current year)

  • Next to ‘Taal’ select ‘English’
  • Next to ‘Opleiding’ select Bachelor in de multimedia en de communicatietechnologie – MCT
  • Next to ‘Traject’ select 3ba multimedia and communication technology (for the crash course Machine Learning & AI, also select 2ba multimedia and communication technology)

Course content

Each course is 6 ECTS (=150 hours of work) with 48 course hours in total. The regular ET AI Engineer semester is 30 ECTS 

AI Engineer (FALL)

Machine Learning 6 ECTS

End goal

IoT and machine learning are the driving forces of the fourth industrial revolution that is rapidly transforming the world as we know it today. This will inevitably lead to a (nature) shift in the labor market. Many (repetitive jobs) will be taken over by AI applications. 

However, there will be enormous opportunities for IT professionals with knowledge of machine learning who can integrate smart algorithms and system.

Particular emphasis is placed on the conceptual understanding of how certain algorithms work. It is important to be able to choose the right machine learning algorithms, train them, evaluate them correctly and improve their performance through hyperparameter tuning.

Themes (*)

  • Supervised learning where you learn from labeled data: Linear (multiple) regression with which you can predict continuous outputs. Examples are predicting stock prices, estimating the age of a person based on a picture of the face, predicting risks, making predictions of sales numbers, etc. Classification allows you to divide data into categories. Face recognition, handwriting recognition, cancer detection, predicting whether someone will click on an advertisement or link are just a few examples. Topics and algorithms that are discussed are logistic regression, Support Vector Machines, Naive Bayes, Random Forest Trees and Ensemble learning.
     
  • Unsupervised learning where you get information from non-labeled data. Clustering techniques where you look for similar data. In this way you can discover patterns, relationships and similarities in complex multi-dimensional data. Dimensionality reduction allows us to transform data to the essence. For example, data can be presented more compactly or the performance of machine learning prediction techniques can be increased.
     
  • Neural networks, inspired by how the brain works, allow us to extract insights from data that until recently were not possible. In the machine learning module we look at its conceptual functioning and we build neural networks for regression and classification. This lays the foundation for the deep learning module that builds on this.

Deep Learning 6 ECTS

End goal

The deep learning module continues where the machine learning module has stopped, namely in the neural networks. Applications include natural language processing and sentiment analysis.

Themes (*)

  • Repetition of neural neural networks and introduction to deep learing.
  • Convolutional Neural Networks (CNN) that are mainly used for image recognition.
  • Auto encoders and restricted Bolzmann machines: can reconstruct lost or damaged data but can also be used to generate music or make suggestions.
  • Generative Adversarial Networks (GAN). Used for, for example, image generation, predicting which medicine will work with certain symptoms, etc.
  • Recommendation systems for generating personalized recommendations.
  • Neural networks with memory: Recursive Neural Networks (RNN) and Long Short-term memory networks (LSTM): applications include natural language processing and sentiment analysis.
  • Reinforcement learning: the algorithm learns through interaction with the environment.

AI Engineer Essentials 6 ECTS

In hands-on sessions you’ll acquire the advanced software skills you must master (e.g. containers, Linux…) as an AI Engineer

Basic Dutch 3 ECTS (optional course)

Through short, lively lesson units, you will learn basic Dutch grammar along with typical expressions and introductory vocabulary to help you during your stay in Kortrijk and Flanders. This language course is complementary to the Meeting Flanders Today lessons and gives additional insights into Flemish and Belgian society and culture.

Meeting Flanders today 3 ECST (optional course)

This course will deepen your knowledge and show the most interesting side of the Flanders region (and Belgium in general). Along with international tourism students, we’ll dive into several “typical” Flemish/Belgian themes, such as design, comics, European institutions, cycling and, of course, chocolate and beer. We’ll explore cutting-edge museums and the European parliament, visit various Belgian cities, get guided by experts and reflect on the culture here through discussions, presentations and tourism and journalism related assignments. Please note that this course will cost you between 130 and 150 euro for travel and tours only. All excursions are required.

MLOps 6 ECTS (optional course)

End goal

In deep learning and advanced AI you learn how we can train different AI models. In this module, the AI services & Mlops module, we want to take these AI models into production. Developers use these trained models in their applications. In addition, AI engineers want to constantly improve and retrain their models.

In this module we will elaborate on deploying Machine Learning and Deep Learning models in a Cloud environment via a CI / CD pipeline. End users call on Microservices or AI services via a frontend environment. The various applications end up in queues that are processed by a scalable back-end environment powered by powerful GPUs. This environment is based on Docker containers and is managed by a Kubernetes platform. The established CI / CD pipeline will be scripted via Python, Powershell or other CLI tools.

The CI / CD pipeline is the backbone of the project. The intention is that this pipeline will be managed by Data Scientists, AI Engineers, front and back end developers and the DevOps team. The entire environment will work on Amazon AWS, Microsoft Azure or Google Cloud Platform. This technically driven module is a deep dive on technology such as Docker, Kubernetes, Kubeflow, Server Less computing platforms. At the end of the module you will be able to set up and manage a complete data pipeline.

AI For Healthcare 6 ECTS

You can take up AI for healthcare if you want to study Deep Learning in this specific context. 

End Goal

In this course you will learn how to apply the frequently utilized deep learning algorithms in the context of (bio-)medical) data. How to handle patient data, DNA data and medical images such as x-rays are just a few of the topics that will be covered during this course. Another important aspect that will be explored is interpretable AI, as it is often important in the healthcare sector to know why an algorithm takes a certain decision.  

Themes

  • Introduction to deep learning and neural networks.  
  • Analysing medical images with Convolutional Neural Networks (CNN), e.g. lung segmentation from x-ray images, and interpretable AI. 
  • Auto-encoders: can reconstruct lost or damaged data but it can also be used to detect anomalies.  
  • Generative Adversarial Networks (GAN). Used for, for example, image generation, predicting which medicine will work with certain symptoms, etc. 
  • The central dogma of molecular biology and interpreting DNA data with Long Short-term memory networks.  
  • Using reinforcement learning to determine which treatment course is the best option for a patient. 

Research Project 6 ECTS

End goal

You complete a research project for a company. The projects are situated in the context of AI & machine learning.  Is this only desk research? Of course not: you are required to prove what you state with a working prototype. Challenging!

AI Engineer (SPRING)

Advanced AI 6 ECTS

End goal

The machine learning and deep learning modules already provided you with a solid foundation for the concepts of the modern AI and how to implement it in a practical way. In this specializing module Advanced AI, we build on these foundations and focus on specific application domains such as advanced computer vision, advanced NLP (natural language processing), generative neural networks, belief networks and multiple-input multiple-output systems.

In addition to further deepening of data-driven machine learning systems, we also study and implement reinforcement learning and deep reinforcement learning systems in this course. Instead of learning from data, these self-learning systems use trial & error to look for an optimal strategy that will give them a maximum reward. These (deep) reinforcement learning strategies mainly find applications in self-learning robots, optimization of industrial processes, computer games, self-driving cars and personalized recommendations. In addition, we go deeper into a number of popular optimization and simulation techniques that can significantly improve the performance of your used learning algorithm.

This module is also hand-on with the focus on being able to implement and integrate the AI systems seen in practice. In that respect, it is intended that you come into contact with various state-of-the-art AI frameworks for the development of both deep neural networks and for the design and simulation of (deep) reinforcement learning systems.

Internship 24 ECTS

The internship is the ideal way to test the practical knowledge and experiences. Because a solid company internship requires a certain period of integration, 15 weeks are provided for this. The objective of the internship is twofold: you will gain insight in the business and work on one or more AI business project or research assignments.

To ensure the smooth running of the internship, an internship agreement is completed and signed by the three parties involved prior to the internship (the internship company, the student and the study programme). The internship agreement clearly states the expected tasks and results of the intern (=the project sheet). In general, we impose three conditions on the internship company: professional supervision, your own workplace and a well-defined assignment of a sufficiently high level.

During the internship period, the student is assigned two supervisors: the internship mentor who is responsible for the daily supervision within the company and the internship supervisor who supervises the student from a distance (based on the internship report).

(*)Themes are listed as an indication and may be subject to change

Attending this program as a non-exchange student?

Prerequisites

Prerequisites for Exchange Students

Minimum 3 successfully completed semesters or an equivalent of 90 ECTS in a Bachelor in Computer Sciences. In this semester prior knowledge and experience is obligatory

Prerequisites for All Students

Statistics:
 
Being familiar with the following statistical concepts:

  • Central tendency: mean, median, mode
  • Variability: variance, standard deviation
  • Probability distributions: binomial distribution, poisson distribution and the normal distributions
  • Correlation (of bi-variate data)
  • Simple linear regression
  • Doing simple predictions on time series: moving average, exponential smoothing

Online course

Python programming (for data science):
 
Having basic Python programming knowledge:

You will get an invite to do an online test so the teachers can help you prepare better in case you have some gaps in your prior knowledge We want you to be successful!

Recommended (not obliged) project management experience (eg: WBS, SCRUM, Waterfall, ...)

All students should bring their own laptop, with the following minimum requirements: 16GB RAM memory, i5 processor with VTX and a 64-bit architecture. You can use your preferred operating system: Windows, Linux or Mac OS X.

Click here for Howest language expectations.

More details can be found on www.howest.be/laptops

The course overview above mentions any specific software necessary for the course unit. Howest applies a very strict policy on the use of legal software.

Prerequisites for MLOps course

You need prior knowledge of Docker. Deep learning has to be followed in the same semester as MLOps (or prior knowledge needs to be documented)

Prerequisites for Non-Exchange Students attending as a 1-year short track Bachelor? 

Bachelor degree in Computer Science, Applied Informatics or a closely related field.

Have a look at Creative Technologies and Artificial Intelligence – short track

Timing and location

The course takes place in Kortrijk.

Address of the campus:

Howest, Campus Kortrijk Weide - The Penta 
Sint-Martens-Latemlaan 1B - 8500 Kortrijk

Read more about the timing of our Exchange semesters