Erinevus lehekülje "Machine learning ITI8565" redaktsioonide vahel
Mine navigeerimisribale
Mine otsikasti
| 55. rida: | 55. rida: | ||
[[Media:Lecture_8_part2_Neural_Networks_ML_2020.pdf |Slides]] | [[Media:Lecture_8_part2_Neural_Networks_ML_2020.pdf |Slides]] | ||
| − | == Lecture 9 | + | == Lecture 9 Supervised learning 6 == |
[[Media:Lecture_09_Neural_Networks_ML_2020.pdf |Slides]] | [[Media:Lecture_09_Neural_Networks_ML_2020.pdf |Slides]] | ||
| − | == Lecture 11 | + | == Lecture 11 Supervised learning 6 == |
[[Media:Lecture_10_Practical_ML_2020.pdf |Slides]] | [[Media:Lecture_10_Practical_ML_2020.pdf |Slides]] | ||
| − | == Lecture 12 | + | == Lecture 12 Markov chains and hidden Markov models== |
[[Media:Lecture_12_Hidden_Markov_Models_2020.pdf |Slides]] | [[Media:Lecture_12_Hidden_Markov_Models_2020.pdf |Slides]] | ||
| − | == Lecture | + | == Lecture 13 Trace, explain and interpret == |
[[Media:Lecture_13_Trace_Explain_Interpret_2020.pdf |Slides]] | [[Media:Lecture_13_Trace_Explain_Interpret_2020.pdf |Slides]] | ||
Redaktsioon: 30. aprill 2020, kell 09:06
Spring term 2020
ITI8565: Machine learning
Taught by: Sven Nõmm
EAP: 6.0
NB! Starting 19.03.2020 lectures take place online using MS teams environment!!! ITI8565 Machine learning team
Lecture slides and all other necessary files are shared in
ained.ttu.ee (your account should be linked to your e-mail!)
Also all the files uploaded to the ITI8565 Machine learning team.
Time and place:
Lectures: Thursdays 14:15-15:45 ICT-315 Labs: Thursdays 16:00-17:30 ICT-401
Consultation: By appointment. Please do not hesitate to ask for an appointment.
Additional information: sven.nomm@taltech.ee
Lecture slides will appear here each week AFTER the lecture! Moodle environment @ ained.ttu.ee will be available for registration on 30.01.2020 16:00.
Lectures
Lecture 1 Introduction
Lecture 2 Cluster Analysis 1
Lecture 3 Cluster Analysis 2
Lecture 4 Supervised learning 1
Lecture 5 Supervised learning 2
Lecture 6 Supervised learning 3
Lecture 7 Supervised learning 4
Lecture 8 Supervised learning 5
Lecture 9 Supervised learning 6
Lecture 11 Supervised learning 6
Lecture 13 Trace, explain and interpret
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 the course