| Language of instruction : English |
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Prerequisites
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No sequentiality
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| Degree programme | | Study hours | Credits | P1 SBU | P1 SP | 2nd Chance Exam1 | Tolerance2 | Final grade3 | |
 | Master of Electronics and ICT Engineering Technology | Compulsory | 135 | 5,0 | 135 | 5,0 | Yes | Yes | Numerical |  |
|
| | | Learning outcomes |
- EC
| EC1 - The holder of the degree thinks and acts professionally with an appropriate engineering attitude and continuous focus on personal development, adequately communicates, effectively cooperates, takes into account the economical, ethical, social and/or international context and is hereby aware of the impact on the environment. | | | - DC
| DC-M8 - can evaluate knowledge and skills critically to adjust own reasoning and course of action accordingly. | | | | - BC
| The student interprets the status of a machine learning model, for example high-variance vs high bias, and deduces appropriate actions to further improve the performance of the model. | | | - DC
| DC-M9 - can communicate in oral and in written (also graphical) form. | | | | - BC
| The students makes a report of the findings of a project. This report consists mainly of figures illustrating the performance of the machine learning system. | | | - DC
| DC-M10 - can function constructively and responsibly as member of a (multidisciplinary) team. | | | | - BC
| The students work on a project in teams of two. They need to arrange the work among themselves. | | | - DC
| DC-M12 - shows a suitable engineering attitude. | | | | - BC
| A critical reflection of the project results is asked and the next step the students would tackle. | - EC
| EC3 - The holder of the degree has advanced knowledge of and insight in principles and applications of automation and sensor technology and signal processing and can independently recognise, critically analyse and create methodical solutions for complex, practical design or optimisation problems with eye for data processing and implementation, with attention to topical technological developments. | | | - DC
| DC-M1 - has knowledge of the basic concepts, structures and coherence. | | | | - BC
| Basic machine learning concepts need to be known.
*supervised techniques: classification, regression
*non-supervised techniques, clustering, datareduction
*Recomendersystems | | | - DC
| DC-M2 - has insight in the basic concepts and methods. | | | | - BC
| The student has insights in following techniques:
regression; logistic regression; neural networks; support vector machines; collaborative filtering of recommendersystems; naive Bayesian techniques. | | | - DC
| DC-M3 - can recognize problems, plan activities and perform accordingly. | | | | - BC
| The student can reflect on the selection of the appropriate method in Machine Learning. | | | - DC
| DC-M4 - can gather, measure or obtain information and refer to it correctly. | | | | - BC
| The student is able to look up certain machine learning algorithms in literature or online sources. | | | - DC
| DC-M5 - can analyze problems, logically structure and interpret them. | | | | - BC
| The student is able to set up an machine learning pipeline based on fussy specifications. | | | - DC
| DC-M6 - can select methods and make calculated choices to solve problems or design solutions. | | | | - BC
| Based on the specifications the student can build a machine learning pipeline. | | | - DC
| DC-M7 - can use selected methods and tools to implement solutions and designs. | | | | - BC
| The student can select the most appropriate classification algorithm among the different classification ones. | | | - DC
| DC-M8 - can evaluate knowledge and skills critically to adjust own reasoning and course of action accordingly. | | | | - BC
| The student can evaluate the performance of the machine learning model and can suggest adjustments to improve its performance. | - EC
| EC7 - The holder of the degree has specialist knowledge of and insight in principles and applications within the domains of computer technology and algorithms of programming languages, in which he/she can initiate, plan, critically analyse and create solid solutions with eye for data processing and implementation, with the help of simulation techniques or advanced tools, while being aware of potential mistakes, practical constraints and with attention to the topical technological developments. | | | - DC
| DC-M1 - has knowledge of the basic concepts, structures and coherence. | | | | - BC
| See in EC3. | | | - DC
| DC-M2 - has insight in the basic concepts and methods. | | | | - BC
| See in EC3. | | | - DC
| DC-M3 - can recognize problems, plan activities and perform accordingly. | | | | - BC
| See in EC3. | | | - DC
| DC-M4 - can gather, measure or obtain information and refer to it correctly. | | | | - BC
| See in EC3. | | | - DC
| DC-M5 - can analyze problems, logically structure and interpret them. | | | | - BC
| See in EC3. | | | - DC
| DC-M6 - can select methods and make calculated choices to solve problems or design solutions. | | | | - BC
| See in EC3. | | | - DC
| DC-M7 - can use selected methods and tools to implement solutions and designs. | | | | - BC
| See in EC3. | | | - DC
| DC-M8 - can evaluate knowledge and skills critically to adjust own reasoning and course of action accordingly. | | | | - BC
| See in EC3. |
|
| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
|
- linear regression
- multivariate linear regression
- logistic regression
- neural networks
- bias / variance problems
- k-means clustering
- principle component analysis
- recommender systems
- anomaly detection
|
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|
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|
Lecture ✔
|
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|
|
Small group session ✔
|
|
|
|
Period 1 Credits 5,00
| Evaluation method | |
|
| Written evaluaton during teaching periode | 60 % |
|
| Transfer of partial marks within the academic year | ✔ |
|
|
|
|
|
|
|
|
| Evaluation conditions (participation and/or pass) | ✔ |
|
| Conditions | To pass this course, the student must achieve at least 8.0/20 on both parts of the evaluation (the project and the exam). |
|
|
|
| Consequences | If the student achieves less than 8.0/20 on one of the two parts of the evaluation (the project or the exam), the final mark will be the weighted average of both parts with a maximum of 8/20. |
|
|
|
Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
|
| Explanation (English) | The mark of the project is transferred from the first exam period. If the student did not do this project, he can do a new project in the 2nd exam period.
If the student has achieved a mark less than 10/20 on the project, he can apply for a 2nd exam chance. |
|
|
|
|
|
| Recommended course material |
| |
Massive Open Online Course (MOOC) on Coursera: Machine Learning by Andrew Ng. |
|
|
| Remarks |
| |
The Machine Learning course will use the Coursera Machine Learning Course as a modern textbook.
This course is organized in the form of application lectures. The educational method in this course consists of sessions in which the professor introduces new concepts in interactive lectures, accompanied and interleaved with regular practical experiments undertaken by the students. The practical experimental part applies the new concepts in guided assignments. Furthermore, the student will work out, in a groups of 2, a project concerning machine learning applied in healthcare applications. |
|
|
|
|
|
| Master of Electromechanical Engineering Technology optie automation | Compulsory | 108 | 4,0 | 108 | 4,0 | Yes | Yes | Numerical | |
|
| | | Learning outcomes |
- EC
| EC1 - The holder of the degree thinks and acts professionally with an appropriate engineering attitude and continuous focus on personal development, adequately communicates, effectively cooperates, takes into account the economical, ethical, social and/or international context and is hereby aware of the impact on the environment. | | | - DC
| DC-M8 - The student can evaluate knowledge and skills critically to adjust own reasoning and course of action accordingly. | | | | - BC
| The student interprets the status of a machine learning model, for example high-variance vs high bias, and deduces appropriate actions to further improve the performance of the model. | | | - DC
| DC-M9 - The student can communicate in oral and in written (also graphical) form. | | | | - BC
| The students makes a report of the findings of a project. This report consists mainly of figures illustrating the performance of the machine learning system. | | | - DC
| DC-M10 - The student can function constructively and responsibly as member of a (multidisciplinary) team. | | | | - BC
| The students work on a project in teams of two. They need to arrange the work among themselves. | | | - DC
| DC-M12 - The student shows a suitable engineering attitude. | | | | - BC
| A critical reflection of the project results is asked and the next step the students would tackle. | - EC
| EC5 - The holder of the degree has specialist knowledge of and insight in principles and applications within the domains of material science, production and mechanical design or the domain of automation in which he/she can independently identify and critically analyse unfamiliar, complex design or optimisation problems, and methodologically create solutions with eye for data processing and implementation, with the help of numerical simulation techniques or advanced tools, aware of potential mistakes, practical constraints and with attention to the recent technological developments. | | | - DC
| DC-M1 - The student has knowledge of the basic concepts, structures and coherence. | | | | - BC
| Basic machine learning concepts need to be known. *supervised techniques: classification, regression *non-supervised techniques, clustering, datareduction *Recomendersystems | | | - DC
| DC-M2 - The student has insight in the basic concepts and methods. | | | | - BC
| The student has insights in following techniques: regression; logistic regression; neural networks; support vector machines; collaborative filtering of recommendersystems; naive Bayesian techniques. | | | - DC
| DC-M3 - The student can recognize problems, plan activities and perform accordingly. | | | | - BC
| The student can reflect on the selection of the appropriate method in Machine Learning. | | | - DC
| DC-M4 - The student can gather, measure or obtain information and refer to it correctly. | | | | - BC
| The student is able to look up certain machine learning algorithms in literature or online sources. | | | - DC
| DC-M5 - The student can analyze problems, logically structure and interpret them. | | | | - BC
| The student is able to set up an machine learning pipeline based on fussy specifications. | | | - DC
| DC-M6 - The student can select methods and make calculated choices to solve problems or design solutions. | | | | - BC
| Based on the specifications the student can build a machine learning pipeline. | | | - DC
| DC-M7 - The student can use selected methods and tools to implement solutions and designs. | | | | - BC
| The student can select the most appropriate classification algorithm among the different classification ones. | | | - DC
| DC-M8 - The student can evaluate knowledge and skills critically to adjust own reasoning and course of action accordingly. | | | | - BC
| The student can evaluate the performance of the machine learning model and can suggest adjustments to improve its performance. |
|
| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
|
- linear regression
- multivariate linear regression
- logistic regression
- neural networks
- bias / variance problems
- k-means clustering
- principle component analysis
- recommender systems
- anomaly detection
|
|
|
|
|
|
|
|
|
Lecture ✔
|
|
|
|
Small group session ✔
|
|
|
|
Period 1 Credits 4,00
| Evaluation method | |
|
| Written evaluaton during teaching periode | 60 % |
|
| Transfer of partial marks within the academic year | ✔ |
|
|
|
|
|
|
|
|
| Evaluation conditions (participation and/or pass) | ✔ |
|
| Conditions | To pass this course, the student must achieve at least 8.0/20 on both parts of the evaluation (the project and the exam). |
|
|
|
| Consequences | If the student achieves less than 8.0/20 on one of the two parts of the evaluation (the project or the exam), the final mark will be the weighted average of both parts with a maximum of 8/20. |
|
|
|
Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
|
| Explanation (English) | The mark of the project is transferred from the first exam period. If the student did not do this project, he can do a new project in the 2nd exam period.
If the student has achieved a mark less than 10/20 on the project, he can apply for a 2nd exam chance. |
|
|
|
|
|
| Recommended course material |
| |
Massive Open Online Course (MOOC) on Coursera: Machine Learning by Andrew Ng. |
|
|
| Remarks |
| |
The Machine Learning course will use the Coursera Machine Learning Course as a modern textbook.
This course is organized in the form of application lectures. The educational method in this course consists of sessions in which the professor introduces new concepts in interactive lectures, accompanied and interleaved with regular practical experiments undertaken by the students. The practical experimental part applies the new concepts in guided assignments. Furthermore, the student will work out, in a groups of 2, a project concerning machine learning applied in healthcare applications. |
|
|
|
|
|
| Master of Software Systems Engineering Technology | Compulsory | 135 | 5,0 | 135 | 5,0 | Yes | Yes | Numerical | |
|
| | | Learning outcomes |
- EC
| EC1 – The Master of Software Engineering Technology can communicate adequately, cooperate effectively, and take into account the economic, ethical, social and/or international context and (s)he is aware of the impact on the environment in all aspects of his/her professional thought-process and agency. (S)he displays an appropriate engineering attitude, including continuous attention to the development of his/her professional competencies --. [people, data literacy and essential software skills]. | | | - DC
| DC-M8 - can evaluate knowledge and skills critically to adjust own reasoning and course of action accordingly. | | | | - BC
| The student interprets the status of a machine learning model, for example high-variance vs high bias, and deduces appropriate actions to further improve the performance of the model. | | | - DC
| DC-M9 - can communicate in oral and in written (also graphical) form. | | | | - BC
| The students makes a report of the findings of a project. This report consists mainly of figures illustrating the performance of the machine learning system. | | | - DC
| DC-M10 - can function constructively and responsibly as member of a (multidisciplinary) team. | | | | - BC
| The students work on a project in teams of two. They need to arrange the work among themselves. | | | - DC
| DC-M12 - shows a suitable engineering attitude. | | | | - BC
| A critical reflection of the project results is asked and the next step the students would tackle. | - EC
| EC3 - The Master of Software Engineering Technology has advanced knowledge and understanding of the principles and applications of software engineering, including software development processes, software architectures and the software life cycle, and can apply them, with an understanding of current technological developments, in complex and practice-oriented problem domains. [software engineering] | | | - DC
| DC-M1 - has knowledge of the basic concepts, structures and coherence. | | | | - BC
| Basic machine learning concepts need to be known. *supervised techniques: classification, regression *non-supervised techniques, clustering, datareduction *Recomendersystems | | | - DC
| DC-M2 - has insight in the basic concepts and methods. | | | | - BC
| The student has insights in following techniques: regression; logistic regression; neural networks; support vector machines; collaborative filtering of recommendersystems; naive Bayesian techniques | | | - DC
| DC-M3 - can recognize problems, plan activities and perform accordingly. | | | | - BC
| The student can reflect on the selection of the appropriate method in Machine Learning. | | | - DC
| DC-M4 - can gather, measure or obtain information and refer to it correctly. | | | | - BC
| The student is able to look up certain machine learning algorithms in literature or online sources. | | | - DC
| DC-M5 - can analyze problems, logically structure and interpret them. | | | | - BC
| The student is able to set up an machine learning pipeline based on fussy specifications. | | | - DC
| DC-M6 - can select methods and make calculated choices to solve problems or design solutions. | | | | - BC
| Based on the specifications the student can build a machine learning pipeline. | | | - DC
| DC-M7 - can use selected methods and tools to implement solutions and designs. | | | | - BC
| The student can select the most appropriate classification algorithm among the different classification ones. | | | - DC
| DC-M8 - can evaluate knowledge and skills critically to adjust own reasoning and course of action accordingly.
| | | | - BC
| The student can evaluate the performance of the machine learning model and can suggest adjustments to improve its performance. | - EC
| EC5 - The Master of Software Engineering Technology masters the necessary sets of specialised knowledge and skills for the design of modular, integrated software systems that, on the basis of data acquisition and data analysis, can make intelligent decisions and that are resilient (secure, robust and scalable), within multidisciplinary projects with an applied research and/or innovation component. [intelligent & resilient systems] | | | - DC
| DC-M1 - has knowledge of the basic concepts, structures and coherence.
| | | | - BC
| See in EC3. | | | - DC
| DC-M2 - has insight in the basic concepts and methods.
| | | | - BC
| See in EC3. | | | - DC
| DC-M3 - can recognize problems, plan activities and perform accordingly. | | | | - BC
| See in EC3. | | | - DC
| DC-M4 - can gather, measure or obtain information and refer to it correctly.
| | | | - BC
| See in EC3. | | | - DC
| DC-M5 - can analyze problems, logically structure and interpret them.
| | | | - BC
| See in EC3. | | | - DC
| DC-M6 - can select methods and make calculated choices to solve problems or design solutions.
| | | | - BC
| See in EC3. | | | - DC
| DC-M7 - can use selected methods and tools to implement solutions and designs.
| | | | - BC
| See in EC3. | | | - DC
| DC-M8 - can evaluate knowledge and skills critically to adjust own reasoning and course of action accordingly.
| | | | - BC
| See in EC3. |
|
| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
|
- linear regression
- multivariate linear regression
- logistic regression
- neural networks
- bias / variance problems
- k-means clustering
- principle component analysis
- recommender systems
- anomaly detection
|
|
|
|
|
|
|
|
|
Lecture ✔
|
|
|
|
Small group session ✔
|
|
|
|
Period 1 Credits 5,00
| Evaluation method | |
|
| Written evaluaton during teaching periode | 60 % |
|
| Transfer of partial marks within the academic year | ✔ |
|
|
|
|
|
|
|
|
| Evaluation conditions (participation and/or pass) | ✔ |
|
| Conditions | To pass this course, the student must achieve at least 8.0/20 on both parts of the evaluation (the project and the exam). |
|
|
|
| Consequences | If the student achieves less than 8.0/20 on one of the two parts of the evaluation (the project or the exam), the final mark will be the weighted average of both parts with a maximum of 8/20. |
|
|
|
Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
|
| Explanation (English) | The mark of the project is transferred from the first exam period. If the student did not do this project, he can do a new project in the 2nd exam period.
If the student has achieved a mark less than 10/20 on the project, he can apply for a 2nd exam chance. |
|
|
|
|
|
| Recommended course material |
| |
Massive Open Online Course (MOOC) on Coursera: Machine Learning by Andrew Ng. |
|
|
| Remarks |
| |
The Machine Learning course will use the Coursera Machine Learning Course as a modern textbook.
This course is organized in the form of application lectures. The educational method in this course consists of sessions in which the professor introduces new concepts in interactive lectures, accompanied and interleaved with regular practical experiments undertaken by the students. The practical experimental part applies the new concepts in guided assignments. Furthermore, the student will work out, in a groups of 2, a project concerning machine learning applied in healthcare applications. |
|
|
|
|
|
| Master of Energy Engineering Technology (new) | Optional | 108 | 4,0 | 108 | 4,0 | Yes | Yes | Numerical | |
|
| | | Learning outcomes |
- EC
| EC1 - The holder of the degree thinks and acts professionally with an appropriate engineering attitude and continuous focus on personal development, adequately communicates, effectively cooperates, takes into account the economical, ethical, social and/or international context and is hereby aware of the impact on the environment. | | | - DC
| DC-M8 - The student can evaluate knowledge and skills critically to adjust own reasoning and course of action accordingly. | | | | - BC
| The student interprets the status of a machine learning model, for example high-variance vs high bias, and deduces appropriate actions to further improve the performance of the model. Wijzig Verwijder | | | - DC
| DC-M9 - The student can communicate in oral and in written (also graphical) form. | | | | - BC
| The students makes a report of the findings of a project. This report consists mainly of figures illustrating the performance of the machine learning system. | | | - DC
| DC-M10 - The student can function constructively and responsibly as member of a (multidisciplinary) team. | | | | - BC
| The students work on a project in teams of two. They need to arrange the work among themselves. | | | - DC
| DC-M12 - The student shows a suitable engineering attitude. | | | | - BC
| A critical reflection of the project results is asked and the next step the students would tackle. | - EC
| EC4 - The holder of the degree has advanced knowledge of and insight in the principles and applications in electrical engineering , possibly complemented with automation or material science and production, in which he/she can independently identify and critically analyse complex, practice-oriented design or optimisation problems, and methodologically create solutions with eye for data processing and implementation and with attention to the recent technological developments. | | | - DC
| DC-M1 - The student has knowledge of the basic concepts, structures and coherence. | | | | - BC
| Basic machine learning concepts need to be known. *supervised techniques: classification, regression *non-supervised techniques, clustering, datareduction *Recomendersystems | | | - DC
| DC-M2 - The student has insight in the basic concepts and methods. | | | | - BC
| The student has insights in following techniques: regression; logistic regression; neural networks; support vector machines; collaborative filtering of recommendersystems; naive Bayesian techniques. | | | - DC
| DC-M3 - The student can recognize problems, plan activities and perform accordingly. | | | | - BC
| The student can reflect on the selection of the appropriate method in Machine Learning. | | | - DC
| DC-M4 - The student can gather, measure or obtain information and refer to it correctly. | | | | - BC
| The student is able to look up certain machine learning algorithms in literature or online sources. | | | - DC
| DC-M5 - The student can analyze problems, logically structure and interpret them. | | | | - BC
| The student is able to set up an machine learning pipeline based on fussy specifications. | | | - DC
| DC-M6 - The student can select methods and make calculated choices to solve problems or design solutions. | | | | - BC
| Based on the specifications the student can build a machine learning pipeline. | | | - DC
| DC-M7 - The student can use selected methods and tools to implement solutions and designs. | | | | - BC
| The student can select the most appropriate classification algorithm among the different classification ones. | | | - DC
| DC-M8 - The student can evaluate knowledge and skills critically to adjust own reasoning and course of action accordingly. | | | | - BC
| The student can evaluate the performance of the machine learning model and can suggest adjustments to improve its performance. |
|
| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
|
- linear regression
- multivariate linear regression
- logistic regression
- neural networks
- bias / variance problems
- k-means clustering
- principle component analysis
- recommender systems
- anomaly detection
|
|
|
|
|
|
|
|
|
Lecture ✔
|
|
|
|
Small group session ✔
|
|
|
|
Period 1 Credits 4,00
| Evaluation method | |
|
| Written evaluaton during teaching periode | 60 % |
|
| Transfer of partial marks within the academic year | ✔ |
|
|
|
|
|
|
|
|
| Evaluation conditions (participation and/or pass) | ✔ |
|
| Conditions | To pass this course, the student must achieve at least 8.0/20 on both parts of the evaluation (the project and the exam). |
|
|
|
| Consequences | If the student achieves less than 8.0/20 on one of the two parts of the evaluation (the project or the exam), the final mark will be the weighted average of both parts with a maximum of 8/20. |
|
|
|
Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
|
| Explanation (English) | The mark of the project is transferred from the first exam period. If the student did not do this project, he can do a new project in the 2nd exam period.
If the student has achieved a mark less than 10/20 on the project, he can apply for a 2nd exam chance. |
|
|
|
|
|
| Recommended course material |
| |
Massive Open Online Course (MOOC) on Coursera: Machine Learning by Andrew Ng. |
|
|
| Remarks |
| |
The Machine Learning course will use the Coursera Machine Learning Course as a modern textbook.
This course is organized in the form of application lectures. The educational method in this course consists of sessions in which the professor introduces new concepts in interactive lectures, accompanied and interleaved with regular practical experiments undertaken by the students. The practical experimental part applies the new concepts in guided assignments. Furthermore, the student will work out, in a groups of 2, a project concerning machine learning applied in healthcare applications. |
|
|
|
|
|
| Master of Teaching in Sciences and Technology - Engineering and Technology choice for subject didactics engineering & technology | Optional | 108 | 4,0 | 108 | 4,0 | Yes | Yes | Numerical | |
|
| | | Learning outcomes |
- EC
| ENG&TECH 2. The newly graduated student has advanced knowledge of and insight into the acquired specific subject didactics and is able to creatively conceive, plan and implement these in an educational context, in particular as an integrated part of a methodologically and project-based series of actions within a multidisciplinary STEM project with a significant component of research and/or innovation. | - EC
| ENG&TECH 3. The newly graduated student has advanced or specialised knowledge of and insight into the principles, structure and technologies of various industrial processes and techniques relevant to his/her specific subject didactics and can autonomously recognise, critically analyse and find methodical and well-founded solutions to complex, multidisciplinary, unfamiliar, practice-oriented design or optimisation problems with an eye to application, selection of materials, automation, safety, environment and sustainability, an awareness of practical limitations and attentiveness to current technological developments. |
|
| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
|
- linear regression
- multivariate linear regression
- logistic regression
- neural networks
- bias / variance problems
- k-means clustering
- principle component analysis
- recommender systems
- anomaly detection
|
|
|
|
|
|
|
|
|
Lecture ✔
|
|
|
|
Small group session ✔
|
|
|
|
Period 1 Credits 4,00
| Evaluation method | |
|
| Written evaluaton during teaching periode | 60 % |
|
| Transfer of partial marks within the academic year | ✔ |
|
|
|
|
|
|
|
|
| Evaluation conditions (participation and/or pass) | ✔ |
|
| Conditions | To pass this course, the student must achieve at least 8.0/20 on both parts of the evaluation (the project and the exam). |
|
|
|
| Consequences | If the student achieves less than 8.0/20 on one of the two parts of the evaluation (the project or the exam), the final mark will be the weighted average of both parts with a maximum of 8/20. |
|
|
|
Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
|
| Explanation (English) | The mark of the project is transferred from the first exam period. If the student did not do this project, he can do a new project in the 2nd exam period.
If the student has achieved a mark less than 10/20 on the project, he can apply for a 2nd exam chance. |
|
|
|
|
|
| Recommended course material |
| |
Massive Open Online Course (MOOC) on Coursera: Machine Learning by Andrew Ng. |
|
|
| Remarks |
| |
The Machine Learning course will use the Coursera Machine Learning Course as a modern textbook.
This course is organized in the form of application lectures. The educational method in this course consists of sessions in which the professor introduces new concepts in interactive lectures, accompanied and interleaved with regular practical experiments undertaken by the students. The practical experimental part applies the new concepts in guided assignments. Furthermore, the student will work out, in a groups of 2, a project concerning machine learning applied in healthcare applications. |
|
|
|
|
|
| Exchange Programme Engineering Technology | Optional | 135 | 5,0 | 135 | 5,0 | Yes | Yes | Numerical | |
|
| |
|
- linear regression
- multivariate linear regression
- logistic regression
- neural networks
- bias / variance problems
- k-means clustering
- principle component analysis
- recommender systems
- anomaly detection
|
|
|
|
|
|
|
|
|
Lecture ✔
|
|
|
|
Small group session ✔
|
|
|
|
Period 1 Credits 5,00
| Evaluation method | |
|
| Written evaluaton during teaching periode | 60 % |
|
| Transfer of partial marks within the academic year | ✔ |
|
|
|
|
|
|
|
|
| Evaluation conditions (participation and/or pass) | ✔ |
|
| Conditions | To pass this course, the student must achieve at least 8.0/20 on both parts of the evaluation (the project and the exam). |
|
|
|
| Consequences | If the student achieves less than 8.0/20 on one of the two parts of the evaluation (the project or the exam), the final mark will be the weighted average of both parts with a maximum of 8/20. |
|
|
|
Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
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| Explanation (English) | The mark of the project is transferred from the first exam period. If the student did not do this project, he can do a new project in the 2nd exam period.
If the student has achieved a mark less than 10/20 on the project, he can apply for a 2nd exam chance. |
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| Recommended course material |
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Massive Open Online Course (MOOC) on Coursera: Machine Learning by Andrew Ng. |
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| Remarks |
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The Machine Learning course will use the Coursera Machine Learning Course as a modern textbook.
This course is organized in the form of application lectures. The educational method in this course consists of sessions in which the professor introduces new concepts in interactive lectures, accompanied and interleaved with regular practical experiments undertaken by the students. The practical experimental part applies the new concepts in guided assignments. Furthermore, the student will work out, in a groups of 2, a project concerning machine learning applied in healthcare applications. |
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1 examination regulations art.1.3, section 4. |
| 2 examination regulations art.4.7, section 2. |
3 examination regulations art.2.2, section 3.
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| Legend |
| SBU : course load | SP : ECTS | N : Dutch | E : English |
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