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 30 ECTS
Machine Learning  (crash course)
Supervised, unsupervised learning and neural network
6 ECTS
Deep Learning
neural networks incl NLP and sentiment analysis
6 ECTS
Advanced AI 6 ECTS
AI Cloud Services & MLops 6 ECTS
Research Project 
complete a research project for a company
6 ECTS

 

OPTIONAL EVENING COURSE 3 ECTS
English for exchange students
This English course provides exchange students with an intensive training in speaking, listening, reading and writing skills, all focused on their ongoing or upcoming academic experience. The lecturer will take the diversity of academic areas of the participants into account, by addressing a range of topics and choosing subjects that are relevant to the attending students wherever possible. The course aims at the B2 proficiency level and uses the IELTS methodology. We strongly recommended it to all exchange students who have not fully achieved the B2-level in English at the moment of application for their mobility project. For the more proficient students, we still see the course as a useful immersion experience.
3 ECTS

 

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 

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.

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.

AI Cloud Services & MLops  6 ECTS

  • 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.

Research Project 6ECTS

  • 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!

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

Prerequisites

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 machine learning & deep learning

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.

Non-exchange students can only attend the international semester as a Postgraduate course and need to have completed at least a full Bachelor degree programme at the time they start the international semester course at Howest.
 

Timing and location

The course takes place in Kortrijk.

Our Autumn semester runs from 14 September 2020 to 29 January 2021

The first week is the fulltime Orientation Week. The actual lessons start on 21 September. From 02/11/2020 to 07/11/2020 there is one week of Autumn Holiday. Winter holidays are from 21/12/2020 to 02/01/2021.
The examination period starts immediately afterwards. In the last week of the semester (25/01-30/01), there are still exams, as well as feedback sessions for students.

The Howest 2nd  chance exams take place in the 3rd exam period (Aug/Sept). As an exceptional service for exchange students AND only after explicit approval by the head of the study department, a 2nd  exam can be taken by the end of the 1st  exam period, at a date to be determined by the study department.


 

Address of the campus

Howest Kortrijk
Graaf Karel de Goedelaan 5, Kortrijk, Belgium

 

Opleiding / Dienst