Machine learning algorithms (TIETS39)

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NEW (at 21.10, 6th April) !  Since the examinations had to be cancelled, one substitutes the examination by writing an essay from one of the given topics. The instructions are given here:

MachineLearningAlgorithmsEssayInstructions2020

The essay has to be sent no later than on the 20th May to Martti Juhola.

Contents

Machine learning algorithms are presented. Their use to build computational models are described.

Procedure

    • Advanced study course, 5 ECTS
    • 12 lectures (24 h), Martti Juhola
    • 5 weekly exercise times (10 h), Jyrki Rasku
    • Additional scores: when one makes more of all weekly exercises than 30%, additional scores [0,5] are given as follows: ≥30% 0, ≥41% 1, ≥52% 2, ≥63% 3, ≥74% 4, ≥85% 5
    • To pass the course: (1) at least 30% of weekly exercises have to be made and (2) the examination with scores of 12 or more from [0,30]

 Matlab is used for exercises.

To load Matlab you first need an activation code and obtaining this may take time. (You also need a TAU account.) To be done via the following link, but if no more valid, search for from IT services of TAU:

https://intra.tuni.fi/en/handbook/2677/2703/3234

 

Lectures

The first Tuesday lecture on the 3rd March will be in Pinni B1097.

On Tuesdays at 10-12 from the 10th March to the 7th April 2020, venue Pinni B4113

On Wednesdays at 10-12 from the 4th March to the 8th April 2020, venue Main Building C6

Join Zoom meeting, the 8th April:

https://tuni.zoom.us/j/661241456

Meeting ID: 661 241 456

 

Weekly exercises

On Thursdays at 8-10 from the 12th March to the 16th April excluding the 9th April 2020, venue Main Building C6

Especial 1

Please send you exercise solutions by email to Jyrki.Rasku@tuni.fi provided that you do this before each “exercise” Thursday: 19th, 26th March, 2nd or

16th  9th April.  NOTE THE CHANGE!

While sending your exercise solutions email, please write in the title of the email which exercise time they concern, e.g., “Machine learning algorithms: exercise solutions for the 26th March”.

Especial 2; information at 21.40, the 12th March

According to the new instructions, all later exercises will be handled by sending the solutions by email to Jyrki.Rasku@tuni.fi by each Wednesday evening.

At the moment, conventional lectures are not allowed to be arranged. The lecture material is delivered in this webpage as usual.  

 

Examinations

REMEMBER TO ENROLL YOURSELF ON THE EXAMINATION AT LAST 8 DAYS BEFORE THE DATE!

On the 29th April,12th May or 27th May 2020

 

Materials

Lectures:machine learning algorithms 2020 Contents, machinelearningalgorithms0, machinelearningalgorithms1, machinelearningalgorithms2, machinelearningalgorithms3,

machinelearningalgorithms4machinelearningalgorithms5 , machinelearningalgorithms6, machinelearningalgorithms7machinelearningalgorithms8,

machinelearningalgorithms9machinelearningalgorithms10

Please do not give recordings to anyone outside TUNI!

Lecture recordings

17th March: https://panopto.tuni.fi/Panopto/Pages/Viewer.aspx?id=41d000c4-da16-493c-9ae7-ab8300a144a9

18th March: https://panopto.tuni.fi/Panopto/Pages/Viewer.aspx?id=3029501a-20fc-4a37-be44-ab8300a31030

24th March: https://panopto.tuni.fi/Panopto/Pages/Viewer.aspx?id=689bd7f0-90e2-4983-a697-ab8800c9ace8

25th March: https://panopto.tuni.fi/Panopto/Pages/Viewer.aspx?id=2ca4d684-34b7-4da7-b5b1-ab8900ae0da7

31st March: https://panopto.tuni.fi/Panopto/Pages/Viewer.aspx?id=7e489b10-07b5-49b0-b68e-ab8f0096c626

1st April: https://panopto.tuni.fi/Panopto/Pages/Viewer.aspx?id=0e4117ef-0495-42a6-89b8-ab900094e1a2

7th April: https://panopto.tuni.fi/Panopto/Pages/Viewer.aspx?id=acf756ee-1ed0-422f-94b8-ab96009691d9

8th April: https://panopto.tuni.fi/Panopto/Pages/Viewer.aspx?id=02bf3808-9d7a-467a-b196-ab9700a2554d

NOTE that along with the current special arrangements of teaching the last exercise time was changed from the 16th to 9th April. However, since the change was made late, you can send your solutions up to the 16th April.

Weekly exercises: MLAE1, MLAE2,  MLAE3, MLAE4, MLAE5

Data sets: Data

Solutions: MLAE1 Solution outlines, MLAE2 Solution outlines, MLAE3 Solution outlines, MLAE4 solution outlines, MLAE5 solution outlines