Systems analysis and decision support methods in Computer Science (2017/2018)

 

 

Course information

Office hours: Wednesday, 16:00-17:00, 121 C6 (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

 

This semester I teach the following groups:

  • Wednesday (even week), 17:05-18:45, 127a (D-2)
  • Wednesday (odd week), 17:05-18:45, 127a (D-2)
  • Wednesday (even week), 18:55-20:35, 127a (D-2)
  • Wednesday (odd week), 18:55-20:35, 127a (D-2)
  • Thursday (odd week), 17:05-18:45, 107a (D-2)

 

 

Course schedule

Class I: Introduction to Python

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

Class schedule: 

  • Regression problem
  • Linear regression model
  • Overfitting/underfitting [demo]
  • Regularization [demo]
  • Example of regression in 3D [demo]
  • Assignment 1 [pdf][zip]

Additional materials:

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

 

Class III: Models k-NN and Naive Bayes

Class schedule: 

  • Classification problem
  • Model k-NN
  • Model Naive Bayes
  • Evaluation of assignment 1
  • Assignment 2 [pdf][zip] (last update 5.04.2018)

Additional materials:

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

Class IV: Logistic regresion

Class schedule: 

  • Logistic regression model
  • Evaluation of assignment 2
  • Assignment 3 [pdf][zip](last update 5.04.2018)

Additional materials:

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

 

Class V: Final assignment

Class schedule: 

  • Presentation [pdf]
  • Final assignment [pdf][zip]
  • 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

 

SELECTED PROJECTS

 

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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RECENT WORK

 

2018/6/6

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 glyconmrsearch.nmrhub.eu

 

2018/5/28

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

 

 

2018/3/4

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

 

CONTACT DETAILS

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