Intelligent Systems

Weekly outline

• 

  General

  • About the course                                                                                                                                            
    

  • Announcements Forum
  • Discussion forum
  • Course description and students obligations 2020/21 File
  • Lectures                                                                                                                                                 
    

  • Lecture slides 2021/22 Folder
  • The videos of the 2020/21 lectures are available in MS Teams, UL FRI Intelligent systems team, use the code 6zwq5bg for access. URL
  • Literature and readings Folder
  • Exams                                                                                                                                                    
    

  • Sample of written exams Folder
  • A sample of oral exam questions File Uploaded 15/01/21, 21:24
  • Zoom link to oral exams URL
  • Schedule for the oral exams on 04 February 2022 Scheduler
  • Exam dates:
    25 January 2022
    04 February 2022
    31 August 2022
    

  • Lab work (general)                                                                                                                           
    

  • Lab work (assignment 1)                                                                                                                  
    

  • Seminar Assignment 1 File Uploaded 1/11/21, 19:52
  • Assignment 1 pairs URL
  • Submission of the first assignment
  • Lab work (assignment 2)                                                                                                                
    

  • Seminar assignment 2 File Uploaded 6/12/21, 13:37
  • Submission of the second assignment
  • Seminar 2 time slots Scheduler
  • Seminar 2 presentations Zoom URL
  • Lab work (Assignment 3)                                                                                                             
    

  • Theoretical seminars File Uploaded 28/12/21, 10:48
  • Seminar 3 sign up sheet URL
  • Additional material                                                                                                                        
    

  • Data analyist is a top job URL
  • R Project homepage URL
  • Data science cheatsheet URL
• 

  4. Oktober - 8. Oktober

  
  

  • Lectures:
    

    • Introduction
    • Overview of the course and introduction to data science
    • Nature inspired computation
    • Genetic algorithms

  • Practical sessions start the 2nd week of the semester.
    

• 

  11. Oktober - 15. Oktober

  
  

  • LECTURES:

    Predictive modeling: introduction
    

  • Labs:

    An introduction to R
    
    

  • Introduction to R (R code) File Uploaded 13/10/19, 18:02
  • Problems (R code) File
  • Solutions (R code) File Uploaded 6/10/19, 20:13
• 

  18. Oktober - 22. Oktober

  
  

  • Lectures:
    Bias and variance
    Simple classifiers: kNN
    

  • Labs:

    Genetic algorithms
    - Data visualization
    
    

  • Introduction to GA (code with additional comments) File Uploaded 18/10/19, 09:56
  • Problems (GA) File
  • "AlgaeLearn.txt" data set File
  • "AlgaeTest.txt" data set File
  • Solutions (GA, R code) File Uploaded 18/10/19, 12:52
• 

  25. Oktober - 29. Oktober

  
  

  • LECTURES:
    Speeding-up similarity search
    Simple prediction models: decision rules
    

    
    

  • Visualization (with ggplot, R code) File Uploaded 20/10/19, 20:05
  • Problems (visualization) File
  • Solutions (visualizations, R code) File
  • "players.txt" data set File
  • "movies.txt" data set File
• 

  1. November - 5. November

  
  

  • LECTURES:
    Feature evaluation and selection
    

  • Classification (R code - updated) File Uploaded 1/11/21, 18:57
  • Problems (R code, with comments) File Uploaded 5/11/19, 11:21
  • "players.txt" data set File
  • "tic-tac-toe-learn.txt" data set File
  • "tic-tac-toe-test.txt" data set File
  • Classification (Solutions) File Uploaded 3/11/19, 19:25
• 

  8. November - 12. November

  
  

  • LECTURES:
    Ensemble learning
    

  • LABS:

    - Classification

    
    

  • Quizz 1
  • Predictive modeling (with parallelism, factors) Datoteka File Uploaded 8/11/21, 12:45
  • Predictive modeling (problems) File Uploaded 10/11/19, 19:18
  • Predictive modeling (Solutions) File
  • "cars.txt" data set File
  • "spamlearn.txt" data set File
  • "spamtest.txt" data set File
  • "vehicle.txt" data set File
  • "student.txt" data set File
• 

  15. November - 19. November

  
  

  • LECTURES:
    Kernel methods and neural networks
    

  • Clustering and attribute evaluation (R code) File Uploaded 16/11/21, 05:23
  • Clustering and attribute evaluation (problems) File Uploaded 25/11/19, 07:07
  • "quadrant.txt" data set File
  • "worldcities.csv" data set File
  • "student.txt" data set File
  • "mushroom.txt" data set File
• 

  22. November - 26. November

  
  

  • LECTURES:
    Natural language processing 1/2
    

  • Quizz 2
  • LABS:
    

    - Ensemble methods
    

• 

  29. November - 3. December

  
  

  • LECTURES:
    Natural language processing 2/2
    

  • LABS:
    Ensemble models
    

  • Ensembles (With comments) File Uploaded 30/11/21, 08:38
  • Ensembles (Problems) File Uploaded 30/11/21, 10:07
  • vehicle.txt data set File
  • "players.txt" data set File
• 

  6. December - 10. December

  
  

  • LECTURES:
    Model explanation methods
    

  • Qizz 3 Quiz
  • Text mining (with parallelism) (R code) File Uploaded 9/12/20, 22:10
  • Text mining problems File Uploaded 8/12/20, 10:16
  • time.mag File Uploaded 4/12/18, 07:20
  • economics-topics File Uploaded 18/12/18, 07:21
  • english.stop File Uploaded 4/12/18, 07:20
  • economics File Uploaded 18/12/18, 07:22
  • earn File Uploaded 8/12/20, 15:57
  • Earn topics File Uploaded 8/12/20, 15:58
• 

  13. December - 17. December

   
  

  • LECTURES:
    Model explanation methods
    

  • LABS: Neural Networks

    
    

    Instructions:

    This lab is Python-based, and requires rather heavy computing resources. The most elegant way to successful execution of the notebook is by using it within the Colab environment: https://colab.research.google.com/

    Simply download the ipynb file provided below and load it within Colab: File -> open notebook -> Upload (rightmost option), and you are good to go!

    
    

  • Deep Learning (Jupyter notebook) File Uploaded 14/12/21, 06:21
  • Deep Learning (source) File Uploaded 14/12/21, 06:21
• 

  20. December - 24. December

  
  

  • LECTURES:
    Reinforcement learning 1/2
    

  • Quizz 4
• 

  27. December - 31. December

   We wish you a Merry Christmas and a Happy New Year!

  
  
• 

  3. Januar - 7. Januar

  
  

  • Lectures:
    Reinforcement learning 2/2
    

  • Quizz 5
• 

  10. Januar - 14. Januar

  Not available
• 

  17. Januar - 21. Januar

  Not available