Master Computer Science profile Health Informatics

Compulsory

108

4,0

108

4,0

Yes

Yes

Numerical

Learning outcomes

EC

EC 1: A graduate of the Master of Computer Science programme has insight into the most important technological developments in the field of computer science and the underlying scientific principles.

EC

EC 2: A graduate of the Master of Computer Science programme is able to keep up with the evolution in the field of computer science (and related fields), to evaluate and to acquire new technologies.

EC

EC 3: A graduate of the Master of Computer Science programme has the necessary knowledge and insights in at least one subdiscipline which allow to contribute to the development and the application of innovative ideas in a certain area of computer science (by deepening basic knowledge at bachelor level, including that of mathematical and other scientific foundations).

DC

Basic machine learning concepts need to be known. *supervised techniques: classification, regression *non-supervised techniques, clustering, datareduction *Recomendersystems

EC

EC 5: A graduate of the Master of Computer Science programme is able to independently model a complex problem in computer science, to introduce the necessary abstractions, to describe and to implement the solution in a structured manner, and, finally, to discuss with the stakeholders why the chosen solution and the corresponding implementation meet with the specifications.

EC

EC 7: A graduate of the Master of Computer Science programme is able to analyse and evaluate information in a critical manner and to process this information efficiently.

EC

EC 8: A graduate of the Master of Computer Science programme is able to communicate information, ideas and solutions to an audience of fellow computer scientists and to non-specialists by expressing him or herself on the proper level of abstraction.

EC

EC 9: A graduate of the Master of Computer Science programme is able to clearly report both orally and verbally on his or her work in a national and international context.

EC

EC 10: A graduate of the Master of Computer Science programme is able to work in team; he or she is able to distribute and coordinate the activities through cooperation in small and large groups.

EC

EC 12: A graduate of the Master of Computer Science programme is able to critically reflect his or her own approach, to account for this and to adjust his/her behaviour accordingly.

EC = learning outcomes DC = partial outcomes BC = evaluation criteria

Content

* linear regression

* multivariate linear regression

* logistic regression

* neural networks

* bias / variance problems

* k-means clustering

* principle component analysis

* recommender systems

* anomaly detection

Organisational and teaching methods

Organisational methods

Lecture ✔

Project ✔

Evaluation

Period 2 Credits 4,00

Evaluation method

Written evaluaton during teaching periode

30 %

Transfer of partial marks within the academic year

✔

Report

✔

Written exam

50 %

Closed-book

✔

Oral explanation

✔

Practical exam

20 %

Second examination period

Evaluation second examination opportunity different from first examination opprt

No

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 an 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 Electronics and ICT Engineering Technology

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 - 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 - 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

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

Content

* linear regression

* multivariate linear regression

* logistic regression

* neural networks

* bias / variance problems

* k-means clustering

* principle component analysis

* recommender systems

* anomaly detection

Organisational and teaching methods

Organisational methods

Lecture ✔

Project ✔

Evaluation

Period 2 Credits 4,00

Evaluation method

Written evaluaton during teaching periode

30 %

Transfer of partial marks within the academic year

✔

Report

✔

Written exam

50 %

Closed-book

✔

Oral explanation

✔

Practical exam

20 %

Second examination period

Evaluation second examination opportunity different from first examination opprt

No

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 an 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

108

4,0

108

4,0

Yes

Yes

Numerical

Content

* linear regression

* multivariate linear regression

* logistic regression

* neural networks

* bias / variance problems

* k-means clustering

* principle component analysis

* recommender systems

* anomaly detection

Organisational and teaching methods

Organisational methods

Lecture ✔

Project ✔

Evaluation

Period 2 Credits 4,00

Evaluation method

Written evaluaton during teaching periode

30 %

Transfer of partial marks within the academic year

✔

Report

✔

Written exam

50 %

Closed-book

✔

Oral explanation

✔

Practical exam

20 %

Second examination period

Evaluation second examination opportunity different from first examination opprt

No

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 an 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.

3rd year Bachelor of Engineering Technology - Electronics and ICT Engineering Technology

Optional

108

4,0

108

4,0

Yes

Yes

Numerical

Learning outcomes

EC

EC1 - The holder of the degree possesses general scientific and technological application-oriented knowledge of the basic concepts, structures and coherence of the specific domain.

EC

EC2 - The holder of the degree possesses general scientific and discipline-related engineering-technical insight in the basic concepts, methods, conceptual frameworks and interdependent relations of the specific domain.

EC

EC3 - The holder of the degree is able to recognize problems independently and can take initiative to plan activities and perform accordingly.

EC

EC4 - The holder of the degree can gather and obtain relevant scientific and/or technical information and/or he/she can measure the necessary information efficiently and conscientiously. Additionally, he/she can make correct references to information.

EC

EC5 - The holder of the degree can analyse unknown, domain-specific problems, subdivide them, structure them logically, determine the preconditions and interpret the data scientifically.

EC

EC6 - The holder of the degree can select and use adequate solution methods to solve unknown, domain-specific problems and can work methodologically and make solid design choices.

EC

EC7 - The holder of the degree can use the selected methods and tools innovatively to systematically implement domain-specific solutions and designs while being aware of practical and economic conditions and company-related implications.

EC

EC8 - The holder of the degree can interpret (incomplete) results, can deal with uncertainties and constraints and can evaluate knowledge and skills critically to adjust own reasoning and course of action accordingly.

EC

EC9 - The holder of the degree can communicate with colleagues in oral and in written form (including in a graphical way) about domain-specific aspects in suited language making use of apt terminology.

EC

EC10 - The holder of the degree can function as member of a (multidisciplinary) team using a constructive and responsible approach.

EC

EC12 - The holder of the degree can act application-oriented and goal-driven and can act academically and professionally with the necessary perseverance and with eye for realism and efficiency, showing a research-oriented attitude towards lifelong learning.

EC = learning outcomes DC = partial outcomes BC = evaluation criteria

Content

* linear regression

* multivariate linear regression

* logistic regression

* neural networks

* bias / variance problems

* k-means clustering

* principle component analysis

* recommender systems

* anomaly detection

Organisational and teaching methods

Organisational methods

Lecture ✔

Project ✔

Evaluation

Period 2 Credits 4,00

Evaluation method

Written evaluaton during teaching periode

30 %

Transfer of partial marks within the academic year

✔

Report

✔

Written exam

50 %

Closed-book

✔

Oral explanation

✔

Practical exam

20 %

Second examination period

Evaluation second examination opportunity different from first examination opprt

No

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 an 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 Computer Science choice

Broadening

108

4,0

108

4,0

Yes

Yes

Numerical

Learning outcomes

EC

EC 1: A graduate of the Master of Computer Science programme has insight into the most important technological developments in the field of computer science and the underlying scientific principles.

EC

EC 2: A graduate of the Master of Computer Science programme is able to keep up with the evolution in the field of computer science (and related fields), to evaluate and to acquire new technologies.

EC

EC 3: A graduate of the Master of Computer Science programme has the necessary knowledge and insights in at least one subdiscipline which allow to contribute to the development and the application of innovative ideas in a certain area of computer science (by deepening basic knowledge at bachelor level, including that of mathematical and other scientific foundations).

EC

EC 5: A graduate of the Master of Computer Science programme is able to independently model a complex problem in computer science, to introduce the necessary abstractions, to describe and to implement the solution in a structured manner, and, finally, to discuss with the stakeholders why the chosen solution and the corresponding implementation meet with the specifications.

EC

EC 6: A graduate of the Master of Computer Science programme is able to independently situate a scientific problem, analyse and evaluate it, to formulate a research question and propose a solution for this in a scientifically substantiated manner.

EC

EC 7: A graduate of the Master of Computer Science programme is able to analyse and evaluate information in a critical manner and to process this information efficiently.

EC

EC 8: A graduate of the Master of Computer Science programme is able to communicate information, ideas and solutions to an audience of fellow computer scientists and to non-specialists by expressing him or herself on the proper level of abstraction.

EC

EC 9: A graduate of the Master of Computer Science programme is able to clearly report both orally and verbally on his or her work in a national and international context.

EC

EC 10: A graduate of the Master of Computer Science programme is able to work in team; he or she is able to distribute and coordinate the activities through cooperation in small and large groups.

EC

EC 12: A graduate of the Master of Computer Science programme is able to critically reflect his or her own approach, to account for this and to adjust his/her behaviour accordingly.

EC = learning outcomes DC = partial outcomes BC = evaluation criteria

Content

* linear regression

* multivariate linear regression

* logistic regression

* neural networks

* bias / variance problems

* k-means clustering

* principle component analysis

* recommender systems

* anomaly detection

Organisational and teaching methods

Organisational methods

Lecture ✔

Project ✔

Evaluation

Period 2 Credits 4,00

Evaluation method

Written evaluaton during teaching periode

30 %

Transfer of partial marks within the academic year

✔

Report

✔

Written exam

50 %

Closed-book

✔

Oral explanation

✔

Practical exam

20 %

Second examination period

Evaluation second examination opportunity different from first examination opprt

No

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 an 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.