Erinevus lehekülje "Machine learning ITI8565 (2017)" redaktsioonide vahel
| 74. rida: | 74. rida: | ||
Home Assignment 1 is available in Moodle! The deadline is 15.03.2016 23:55 ! | Home Assignment 1 is available in Moodle! The deadline is 15.03.2016 23:55 ! | ||
| − | =Lecture 5: = | + | =Lecture 5: Gaussian Mixture Model and EM algorithm = |
[[Media:Lecture5_ML2016_GMM_EM_Clusters.pdf |Slides]] | [[Media:Lecture5_ML2016_GMM_EM_Clusters.pdf |Slides]] | ||
| − | =Lecture 6: | + | =Lecture 6: Neural Networks = |
[[Media:Lecture6_ML2016_Neural_Networks.pdf |Slides]] | [[Media:Lecture6_ML2016_Neural_Networks.pdf |Slides]] | ||
| − | =Lecture 7: | + | =Lecture 7: Logistic Regression = |
[[Media:Lecture7_ML2016_Logistic_Regression.pdf |Slides]] | [[Media:Lecture7_ML2016_Logistic_Regression.pdf |Slides]] | ||
| − | =Lecture 8: | + | =Lecture 8: Competitive learning = |
[[Media:Lecture8_ML2016_Competitive_Learning.pdf |Slides]] | [[Media:Lecture8_ML2016_Competitive_Learning.pdf |Slides]] | ||
| − | =Lecture 9: | + | =Lecture 9: Marcov Models = |
[[Media:Lecture9_ML2016_N_Markov_chains_and_hMm.pdf |Slides]] | [[Media:Lecture9_ML2016_N_Markov_chains_and_hMm.pdf |Slides]] | ||
| − | =Lecture 10: | + | =Lecture 10: Multiclass classification = |
[[Media:Lecture11_ML2015_N_Multiclass_classification.pdf |Slides]] | [[Media:Lecture11_ML2015_N_Multiclass_classification.pdf |Slides]] | ||
| − | Lecture 11: | + | Lecture 11: Support vector machines |
[[Media:Lecture11_ML2016_SVM.pdf |Slides]] | [[Media:Lecture11_ML2016_SVM.pdf |Slides]] | ||
| − | Lecture 12: | + | Lecture 12: Random forests |
[[Media:Lecture12_ML2016_RandomForests.pdf |Slides]] | [[Media:Lecture12_ML2016_RandomForests.pdf |Slides]] | ||
Redaktsioon: 30. juuni 2016, kell 11:20
Previous years: 2015
Spring 2015/2015
ITI8565: Machine learning
Taught by: Sven Nõmm
EAP: 6.0
Time and place: Thursdays
Lectures: 14:00-15:30 ICT-A1 Labs: 16:00-17:30 ICT-402
Preliminary Information:
Examinations and consultations ICT-405:
26.05 Consultation 14:00-15:30
02.06 Exam 1 14:00-15:30
10.06 Exam 2 16:00-17:30
14.06 Make-up Exam 14:00-15:30
Consultation: by appointment TBA
Additional information: sven.nomm@ttu.ee
The course is organised by the Department of Comptuer Science. The course is supported by IT Academy.
Evaluation
- 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 to pass
Lectures
Lecture slides, necessary files, links and other necessary information would appear here before the lecture or practice.
Lecture 1: Introduction and Decision Trees
Practice 1
Please Observe the practice room change starting 12.02.2016 ICT-402 !!!
Lecture 2: k- Nearest Neighbors
Practice 2
Lecture 3: K- Means
NB! Moodle environment for the course has been activated
If you need the code to enroll please contact the teacher by e-mail. I will continue to upload lecture slides here. All other resources including home assignments will be available thorough the moodle only!!!
Lecture 4: Linear Regression
Home Assignment 1
Home Assignment 1 is available in Moodle! The deadline is 15.03.2016 23:55 !
Lecture 5: Gaussian Mixture Model and EM algorithm
Lecture 6: Neural Networks
Lecture 7: Logistic Regression
Lecture 8: Competitive learning
Lecture 9: Marcov Models
Lecture 10: Multiclass classification
Lecture 11: Support vector machines Slides
Lecture 12: Random forests Slides