Erinevus lehekülje "Machine learning ITI8565" redaktsioonide vahel
Mine navigeerimisribale
Mine otsikasti
| 55. rida: | 55. rida: | ||
<pre style="color: red"> | <pre style="color: red"> | ||
| − | Test I!!! | + | 30.03.2023 Test I!!! |
</pre> | </pre> | ||
| + | |||
| + | <pre style="color: red"> | ||
| + | 02.04.2023 23:59 Deadline to submit home assignment II!!! | ||
| + | </pre> | ||
| + | [[Home_Assignment_02_ML_2023_web_version.pdf |Home Assignment II]] | ||
| + | |||
== Week 10 Neural Networks I == | == Week 10 Neural Networks I == | ||
| 70. rida: | 76. rida: | ||
== Week 13 Deep Learning II: Convolutional neural networks== | == Week 13 Deep Learning II: Convolutional neural networks== | ||
| − | + | TBU [[Media:Lecture_14_Deep_Learning_CNN_ML_2022.pdf |Slides]] | |
== Week 14 Deep Learning II: Transformers== | == Week 14 Deep Learning II: Transformers== | ||
| − | + | TBU [[Media:Lecture_15_Transformers_ML_2022.pdf |Slides]] | |
| − | |||
| − | |||
| − | |||
| − | [[Media:Lecture_15_Transformers_ML_2022.pdf |Slides]] | ||
== Week 15 Foundations of eXplainable AI == | == Week 15 Foundations of eXplainable AI == | ||
[[Media:Lecture_15_Trace_Explain_Interpret_2023.pdf |Slides]] | [[Media:Lecture_15_Trace_Explain_Interpret_2023.pdf |Slides]] | ||
| + | <pre style="color: red"> | ||
| + | 14.05.2023 23:59 Deadline to submit home assignment III!!! | ||
| + | </pre> | ||
| + | [[HA_3_ML_2023_web_version.pdf |Home Assignment II]] | ||
| + | |||
| + | == Week 16== | ||
| + | <pre style="color: red"> | ||
| + | 16.05.2023Test II!!! | ||
| + | </pre> | ||
| + | |||
*91 < score -- grade 5 (excellent) | *91 < score -- grade 5 (excellent) | ||
Redaktsioon: 23. jaanuar 2023, kell 16:06
Spring term 2023
ITI8565: Machine learning
Taught by: Sven Nõmm
EAP: 6.0
- The course will continue purely in online mode!!!
Lectures on Tuesdays 15:30-17:00 ICT-A1
Practices on Thursdays 16:30-17:00 ICT-401
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
Week 15 Foundations of eXplainable AI
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