Systems analysis and decision support methods in Computer Science 
(the laboratory class, summer 2019)



Course information

Office hours:

  • Friday, 18:15-20:35, 121 C3 (after e-mail notification) 
  • Wednesday, 18:15-20:35, 121 C3 (after e-mail notification) 

Credit rules: [link]

Recommended literature:

  • Murphy, Kevin P. Machine learning: a probabilistic perspective. MIT press, 2012.
  • Bishop, Ch. Pattern Recognition and Machine Learning. Springer, 2006. 


Additional materials:

  • Python course [link]
  • Machine learning course [link


The link to my teaching calendar (summer semester, all groups) [link]

Results: [link] (link is inactive when the file is modified)


Course schedule

Class I: Introduction to Python

Prerequisites: -

Class schedule: 

  • Safety rules, course credit rules, organizational issues
  • Introduction to Python [link]

Additional materials:

  • Introduction to PyCharm [link]
  • Online Python console [link]


Class II: Linear regression


  • Basic Python syntax

Class schedule: 

  • Regression problem
  • Linear regression model
  • Overfitting/underfitting
  • Regularization
  • Assignment 1 [pdf][zip]

Additional materials:

  • Least squares [link]
  • Murphy, chapters 7.1-7.3


Class III: Models k-NN and Naive Bayes


  • Completed assignment 1

Class schedule: 

  • Classification problem
  • Model k-NN
  • Model Naive Bayes
  • Evaluation of assignment 1

Additional materials:

  • Classification[link]
  • k-NN [link]
  • Naive Bayes [link]
  • Murphy, chapter 3.5

Class IV: Logistic regression


  • Completed assignment 2

Class schedule: 

  • Logistic regression model
  • Evaluation of assignment 2
  • Assignment 3 [pdf][zip]

Additional materials:

  • Gradient descent [link]
  • Logistic regression [link]
  • Murphy, chapter 8.1-8.3


Class V: Final assignment


  • Completed assignment 3

Class schedule:

  • Final assignment (2019) [pdf][zip]
    Deadline 09.06.2019 23:59
  • Evaluation of assignment 3

Additional materials:

  • Neural network [link]


Class VI and VII: Individual work on final assignment

Class schedule: 

  • Discussion about final assignment
  • Discussion about selected machine learning method or model
  • Time for individual work on final assignment





Deep convolutional neural network
for an automated signal selection in protein NMR spectra


Chemical shift-based identification
of oligosaccharide topology

Image analysis for confocal microscopy in the context of 3D liver tissue architecture studies

Commercial projects

X 2014 - ...

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II 2016 - VI 2018

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VII 2013 - VII 2014

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Our manuscript "Chemical shift-based identification of monosaccharide spin-systems with NMR spectroscopy to complement untargeted glycomics" has been accepted in Bioinformatics. 

Open access article is available here. The algorithm proposed in the paper is accessible by the web page



Our manuscript "Towards fully automated protein structure elucidation with NMR spectroscopy" has been accepted at BOOM workshop (ICML 2018).




Our manuscript "NMRNet: A deep learning approach to automated peak picking of protein NMR spectra" has been accepted in Bioinformatics.



Building C-3, room 121
Department of Computer Science
Wrocław University of Technology
Wybrzeże Stanisława Wyspiańskiego 27
50-370 Wrocław, Poland