Erinevus lehekülje "Machine learning ITI8565" redaktsioonide vahel
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[[Media:lecture_05_supervised_learning_2_ml_2024.pdf |Slides]] | [[Media:lecture_05_supervised_learning_2_ml_2024.pdf |Slides]] | ||
| − | [[Media:HA_01_ML_2023_web_version.pdf |Home Assignment I]] | + | <!--[[Media:HA_01_ML_2023_web_version.pdf |Home Assignment I]] --> |
== Week 6 Supervised learning III: Gradient descent == | == Week 6 Supervised learning III: Gradient descent == | ||
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== Week 9 Markov Models == | == Week 9 Markov Models == | ||
| − | [[Media:Lecture_09_Hidden_Markov_Models_ML2023.pdf |Slides]] | + | [[Media:Lecture_09_Hidden_Markov_Models_ML2023.pdf |Slides]] |
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| − | 30.03.2023 Test I!!! | + | <!--30.03.2023 Test I!!! --> |
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| − | 02.04.2023 23:59 Deadline to submit home assignment II!!! | + | <!--02.04.2023 23:59 Deadline to submit home assignment II!!! --> |
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| − | [[Media:Home_Assignment_02_ML_2023_web_version.pdf |Home Assignment II]] | + | <!--[[Media:Home_Assignment_02_ML_2023_web_version.pdf |Home Assignment II]] --> |
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| − | [[Media: HA_3_ML_2023_web_version.pdf |Home Assignment III]] | + | <!--[[Media: HA_3_ML_2023_web_version.pdf |Home Assignment III]] --> |
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Redaktsioon: 19. jaanuar 2024, kell 13:09
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: You are welcome to join the course by means of ÕIS! On January the 29thth around afternoon ÕIS will generate welcome e-mail with the instructions to join Moodle page of the course.
Slides below are mostly 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
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
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
- 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