CS-E4075 - Special Course in Machine Learning, Data Science and Artificial Intelligence D: Gaussian processes - theory and applications, 11.01.2021-19.02.2021

Topic outline

• Assignments
  Five weekly practical assignments (Python Notebooks) with finished assignments returned before in mycourses the weekly exercise session:
  

  • H1: wednesdays 12:15-14:00 (first session Jan 20th)
  • H2: fridays 12:15-14:00 (first session Jan 22th)

  Note that you only need to attend one session per week. There is no session on the first week.
  
  The course is graded only by completed and returned assignments.  The assignments are graded with 0/0.5/1 points per task per assignment. An extra 1p will be granted if you are present and prepared to present your solution in a session.
  
  The assignments are released on mondays.
  
  

  Assignment #1: deadline wednesday, January 20th, 12:00

  Assignment #2: deadline wednesday, January 27th, 12:00

  Assignment #3: deadline wednesday, February 3rd, 12:00

  Assignment #4: deadline wednesday, February 10th, 12:00
  

  Assignment #5: deadline wednesday, February 17th, 12:00

  
  

  • Select activity Assignment 1

    

    Assignment 1

    Return the completed .ipynb file (or pdf). The file is a Python Jypyter notebook, make sure you have python and jupyter installed (eg. use conda package). See jupyter.org for help.

    
    

    The assignment is based on lectures 1 and 2.

  • Select activity Assignment 2

    

    Assignment 2

    Return completed .ipynb file (or pdf). The assignment is based on lectures 2 and 3.

  • Select activity Assignment 3

    

    Assignment 3

    Based on lectures 4 (VI) and 5 (GPLVM). Return the completed notebook (or pdf).

  • Select activity Assignment 4

    

    Assignment 4

    Return completed notebook. The tasks cover lectures 6 and 7.

  • Select activity lab3 solution with bijection

    

    lab3 solution with bijection File IPYNB
  • Select activity Assignment 5

    

    Assignment 5

    This covers lectures 8 (deep GPs) and 9 (model selection).