Erinevus lehekülje "Machine learning ITI8565" redaktsioonide vahel
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| 1. rida: | 1. rida: | ||
[[Machine learning ITI8565]] | [[Machine learning ITI8565]] | ||
| − | Spring term | + | Spring term 2024 |
ITI8565: Machine learning | ITI8565: Machine learning | ||
| 9. rida: | 9. rida: | ||
EAP: 6.0 | EAP: 6.0 | ||
| − | Lectures on Tuesdays | + | Lectures on Tuesdays 12:00-17:00 ICT-A2 |
| − | Practices on Thursdays | + | Practices on Thursdays 14:00-15:30 ICT-401 |
Consultations is by appointment only! Please do not hesitate to ask for consultation! | Consultations is by appointment only! Please do not hesitate to ask for consultation! | ||
<pre style="color: red"> | <pre style="color: red"> | ||
| − | + | Information for perspective students: | |
| + | This page will be populated with the up to date lecture slides during the month of January. | ||
| + | You are welcome to join the course by means of ÕIS! | ||
| + | On January the 30th around afternoon ÕIS will generate welcome e-mail with the instructions to join Moodle page of the course. | ||
| + | |||
</pre> | </pre> | ||
<pre style="color: red"> | <pre style="color: red"> | ||
| − | + | Slides below are from the year 2023. You are welcome to use this material as the reference but be aware that this year the course content will be revised and a few news topics will be added. | |
</pre> | </pre> | ||
Redaktsioon: 2. jaanuar 2024, kell 16:05
Spring term 2024
ITI8565: Machine learning
Taught by: Sven Nõmm
EAP: 6.0
Lectures on Tuesdays 12:00-17:00 ICT-A2
Practices on Thursdays 14:00-15:30 ICT-401
Consultations is by appointment only! Please do not hesitate to ask for consultation!
Information for perspective students: This page will be populated with the up to date lecture slides during the month of January. You are welcome to join the course by means of ÕIS! On January the 30th around afternoon ÕIS will generate welcome e-mail with the instructions to join Moodle page of the course.
Slides below are from the year 2023. You are welcome to use this material as the reference but be aware that this year the course content will be revised and a few news topics will be added.
Lectures
Week 1 Introduction, Distance function
Week 2 Cluster analysis I
Week 3 Cluster analysis II (Probabilistic approach; Outlier and Anomaly Analysis)
Week 4 Supervised learning I: Classification
Week 5 Supervised learning II: Regression
05.03.2023 23:59 Deadline to submit home assignment I!!!
Week 6 Supervised learning III: Gradient descent
Week 7 Supervised learning IV: Support Vector Machine
Week 8 Supervised learning V: Model quality boosting
Week 9 Markov Models
30.03.2023 Test I!!!
02.04.2023 23:59 Deadline to submit home assignment II!!!
Week 10 Neural Networks I
Week 11 Neural Networks II
Week 12 Deep Learning I: Sequential Models
TBP
Week 13 Deep Learning II: Convolutional neural networks
TBU Slides
Week 14 Deep Learning II: Transformers
TBU Slides
14.05.2023 23:59 Deadline to submit home assignment III!!!
Week 16
16.05.2023Test II!!!
- 91 < score -- grade 5 (excellent)
- 81 < score < 90 -- grade 4 (very good)
- 71 < score < 80 -- grade 3 (good)
- 61 < score < 70 -- grade 2 (satisfactory)
- 51 < score < 60 -- grade 1 (acceptable)
score ≤ 50 -- a student has failed the course