Erinevus lehekülje "Machine learning ITI8565" redaktsioonide vahel
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Mine otsikasti
| 33. rida: | 33. rida: | ||
[[Media:Lecture_03_2_anomaly_and_otlier_analysis_ML2022.pdf |Slides]] | [[Media:Lecture_03_2_anomaly_and_otlier_analysis_ML2022.pdf |Slides]] | ||
| − | == Week 4 Supervised learning I == | + | == Week 4 Supervised learning I: Feature selection kNN and regression == |
[[Media:Lecture_04_Classification_1_ML_2022.pdf |Slides]] | [[Media:Lecture_04_Classification_1_ML_2022.pdf |Slides]] | ||
| − | == Week 5 Supervised learning II == | + | == Week 5 Supervised learning II: Regression and decision trees == |
[[Media:Lecture_05_Supervised_Learning_2_ML_2022.pdf |Slides]] | [[Media:Lecture_05_Supervised_Learning_2_ML_2022.pdf |Slides]] | ||
| + | |||
| + | == Week 6 Supervised learning III: Gradient descent == | ||
| + | [[Media:Lecture_6_Gradient_descent_andmore_ML_2022.pdf |Slides]] | ||
Redaktsioon: 28. veebruar 2022, kell 13:13
Spring term 2022
ITI8565: Machine learning
Taught by: Sven Nõmm
EAP: 6.0
For the month of March the course will continue purely in online mode!!!
Lectures on Tuesdays 13:45-15:15 Online only in MS Teams environment
Practices on Thursdays 13:40-15:10 Online only in MS Teams environment
Please use code HAL900 to join TalTech Moodle page of the course.
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: Feature selection kNN and regression
Week 5 Supervised learning II: Regression and decision trees
Week 6 Supervised learning III: Gradient descent
- 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