Intelligent Systems

Weekly outline

• 

  General

  • About the course                                                                                                                                            
    

  • Announcements Forum
  • Discussion forum
  • Course description and students obligations 2022/23 File
  • Lectures                                                                                                                                                 
    

  • The course syllabus.

    Topic	Lectures
    1	Syllabus and obligations
    2	Nature inspired computation, genetic algorithms
    3	Predictive modeling
    4	Bias and variance
    5	Feature evaluation and selection
    6	Ensemble learning
    7	Kernel methods
    8	Neural networks
    9	Natural language processing
    10	Model explanation methods
    11	Reinforcement learning

    
    

    
    

  • Live stream of Intelligent systems lectures Zoom meeting
  • Lecture slides 2021/22 (continuously updated) Folder
  • Some recordings of the 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
  • Exam dates:
    20 January 2023 at 13:00 in PA
    01 February 2023 at 10:00 in PA
    28 August 2023
    

  • Lab work (general)                                                                                                                           
    

  • Friday morning labs zoom link (7.15AM onwards) URL
  • Lab work (assignment 1)                                                                                                                  
    

  • Seminar 1 File Uploaded 19/10/22, 17:18
  • mazes File Uploaded 19/10/22, 16:52
  • Assignment 1 pairs URL
  • Assignment 1 time slots Scheduler
  • Submission of the first assignment
  • Lab work (assignment 2)                                                                                                                
    

  • Seminar 2 File Uploaded 24/11/22, 17:29
  • train File Uploaded 24/11/22, 17:32
  • test File Uploaded 24/11/22, 17:32
  • Assignment 2 time slots Scheduler
  • Submission of the second assignment
  • Assignment 2 pairs URL
  • Lab work (Assignment 3)                                                                                                             
    

  • Additional material                                                                                                                        
    

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

  • Quiz 1
  • Quiz 2
  • Quiz 3
  • Quiz 4
  • Quiz 5
• 

  3. Oktober - 7. Oktober

  
  

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

• 

  10. Oktober - 14. Oktober

  
  

  • 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
• 

  17. Oktober - 21. Oktober

  
  

  • 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
• 

  24. Oktober - 28. Oktober

  
  

  • 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
• 

  31. Oktober - 4. November

  
  

  • 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
• 

  7. November - 11. November

  
  

  • LABS:

    - Classification

    
    

  • 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
• 

  14. November - 18. November

  
  

  • 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
  • Clustering and attribute evaluation (solutions) File Uploaded 24/11/19, 18:53
  • "quadrant.txt" data set File
  • "worldcities.csv" data set File
  • "student.txt" data set File
  • "mushroom.txt" data set File
• 

  21. November - 25. November

  
  

  • LABS:
    

    - Ensemble methods
    

• 

  28. November - 2. December

  
  

  • LABS:
    Ensemble models
    

  • Ensembles (With comments) File Uploaded 30/11/21, 08:38
  • Ensembles (Problems) File Uploaded 30/11/21, 10:07
  • Ensembles (solutions) File Uploaded 6/12/19, 12:19
  • vehicle.txt data set File
• 

  5. December - 9. December

  
  

  • 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
• 

  12. December - 16. December

   
  

  • 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
• 

  19. December - 23. December

  
  
• 

  26. December - 30. December

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

  
  
• 

  2. Januar - 6. Januar

  
  

  • 
    

    Labs: Model Explainability
    

    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!

  • Model_explainability File Uploaded 2/01/23, 21:42
• 

  9. Januar - 13. Januar

  Not available
• 

  16. Januar - 20. Januar

  Not available