Machine learning (5596)
  
Coordinating lecturer :dr. Nikolaos TSIOGKAS 

Language of instruction : English
Credits: 5,0
  
Period: semester 1 (5sp)
  
2nd Chance Exam1: Yes
  
Final grade2: Numerical
Transitional curriculum
 
Sequentiality
 
   No sequentiality

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 ✔  
Small group session ✔  
Teaching methods  
Exercises ✔  
Homework ✔  
Report ✔  


Evaluation

Semester 1 (5,00sp)

Evaluation method
Oral evaluation during teaching period20 %
Transfer of partial marks within the academic year✔
Open questions✔
Presentation✔
Written exam80 %
Closed-book ✔
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
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 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.


Learning outcomes
  EC = learning outcomes      DC = partial outcomes      BC = evaluation criteria  
Offered inTolerance3
Exchange Programme Engineering Technology J
Master of Electronics and ICT Engineering Technology J
Master of Software Systems Engineering Technology J



1   Education, Examination and Legal Position Regulations art.12.2, section 2.
2   Education, Examination and Legal Position Regulations art.15.1, section 3.
3   Education, Examination and Legal Position Regulations art.16.9, section 2.