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 2023 |
ITI8565: Machine learning | ITI8565: Machine learning | ||
| 9. rida: | 9. rida: | ||
EAP: 6.0 | EAP: 6.0 | ||
| − | <pre style="color: red"> | + | #<pre style="color: red"> |
| − | The course will continue purely in online mode!!! | + | #The course will continue purely in online mode!!! |
| − | </pre> | + | #</pre> |
| − | |||
| − | |||
| − | + | Lectures on Tuesdays 15:30-17:00 ICT-A1 | |
| − | + | Practices on Thursdays 16:30-17:00 ICT-401 | |
| 23. rida: | 21. rida: | ||
== Week 1 Introduction, Distance function == | == Week 1 Introduction, Distance function == | ||
| − | [[Media: | + | [[Media:Lecture_1_Intorduction_and_Distance_function_ML_2023.pdf |Slides]] |
== Week 2 Cluster analysis I == | == Week 2 Cluster analysis I == | ||
| − | [[Media: | + | [[Media:Lecture_02_Cluster_Analysis_1_ML_2023.pdf |Slides]] |
== Week 3 Cluster analysis II (Probabilistic approach; Outlier and Anomaly Analysis) == | == Week 3 Cluster analysis II (Probabilistic approach; Outlier and Anomaly Analysis) == | ||
| − | [[Media: | + | [[Media:Lecture_03_1_Cluster_Analysis_2_Probabilistic_approachML_2023.pdf |Slides]] |
| + | |||
| + | [[Media:Lecture_03_2_anomaly_and_otlier_analysis_ML2023.pdf |Slides]] | ||
| − | [[Media: | + | == Week 4 Supervised learning I: Classification == |
| + | [[Media:Lecture_04_Classification_1_ML_2023.pdf |Slides]] | ||
| − | == Week | + | == Week 5 Supervised learning II: Regression == |
| − | [[Media: | + | [[Media:Lecture_05_Supervised_Learning_2_ML_2023.pdf |Slides]] |
| − | = | + | <pre style="color: red"> |
| − | [[Media: | + | 05.03.2023 23:59 Deadline to submit home assignment I!!! |
| + | </pre> | ||
| + | [[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 == | ||
| − | [[Media: | + | [[Media:Lecture_06_Gradient_descent_andmore_ML_2023.pdf |Slides]] |
== Week 7 Supervised learning IV: Support Vector Machine == | == Week 7 Supervised learning IV: Support Vector Machine == | ||
| − | [[Media: | + | [[Media:Lecture_07_Support_Vector_Machines_Kernel_Trick_ML_2023.pdf |Slides]] |
| + | |||
| + | == Week 8 Supervised learning V: Model quality boosting == | ||
| + | [[Media:Lecture_08_Model_Quality_Boosting_ML_2023.pdf |Slides]] | ||
| + | |||
| + | == Week 9 Markov Models == | ||
| + | [[Media:Lecture_09_Hidden_Markov_Models_ML2023.pdf |Slides]] | ||
| + | |||
| + | <pre style="color: red"> | ||
| + | Test I!!! | ||
| + | </pre> | ||
| + | |||
| + | == Week 10 Neural Networks I == | ||
| + | [[Media: Lecture_10_Neural_Networks_ML_2023.pdf |Slides]] | ||
| + | [[Media: Lecture_10_part_2_Neural_Networks_ML_2023.pdf |Slides]] | ||
| − | |||
| − | |||
| − | == Week | + | == Week 11 Neural Networks II == |
| − | [[Media: | + | [[Media: Lecture_11_Neural_Networks_2_ML_2023.pdf |Slides]] |
| − | == Week | + | == Week 12 Deep Learning I: Sequential Models== |
| − | + | TBP | |
| − | == Week | + | == Week 13 Deep Learning II: Convolutional neural networks== |
| − | + | TBP | |
| − | == Week | + | == Week 14 Deep Learning II: Transformers== |
| − | + | TBP | |
== Week 14 Neural Networks IV == | == Week 14 Neural Networks IV == | ||
| 64. rida: | 79. rida: | ||
[[Media:Lecture_15_Transformers_ML_2022.pdf |Slides]] | [[Media:Lecture_15_Transformers_ML_2022.pdf |Slides]] | ||
| − | == Week 15 | + | == Week 15 Foundations of eXplainable AI == |
| − | [[Media: | + | [[Media:Lecture_15_Trace_Explain_Interpret_2023.pdf |Slides]] |
*91 < score -- grade 5 (excellent) | *91 < score -- grade 5 (excellent) | ||
Redaktsioon: 23. jaanuar 2023, kell 16:00
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
Test I!!!
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
TBP
Week 14 Deep Learning II: Transformers
TBP
Week 14 Neural Networks IV
Week 15 Foundations of eXplainable AI
- 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