Redaktsioon: 30. jaanuar 2017, kell 09:23

Previous years: 2016

Spring 2016/2017

ITI8565: Machine learning

Taught by: Sven Nõmm

EAP: 6.0

Time and place:

 Lectures: Tuesdays 16:00-17:30  ICT-A1
 Labs:  Thursdays   16:00-17:30  ICT-402

Consultation: by appointment TBA

Additional information: sven.nomm@ttu.ee

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

Lectures

Lecture slides, necessary files, links and other necessary information would be provided by means of Moodle (To be set up by 10.02.2017)

@@ 1. rida: / 1. rida: @@
 Previous years: [https://courses.cs.ttu.ee/pages/Machine_learning 2016]
-Spring 2015/2015
+Spring 2016/2017
 ITI8565: Machine learning
@@ 9. rida: / 9. rida: @@
 EAP: 6.0
-Time and place: Thursdays
+Time and place:
-   Lectures: 14:00-15:30  ICT-A1
+   Lectures: Tuesdays 16:00-17:30  ICT-A1
-   Labs: 16:00-17:30  ICT-402
+   Labs:  Thursdays   16:00-17:30  ICT-402
-Preliminary Information:
+Consultation: by appointment TBA
-Examinations and consultations   ICT-405:
-.05 Consultation 14:00-15:30
-.06 Exam 1       14:00-15:30
-.06 Exam 2       16:00-17:30
-.06 Make-up Exam 14:00-15:30
-  Consultation: by appointment TBA
 Additional information: sven.nomm@ttu.ee
-The course is organised by [http://cs.ttu.ee the Department of Comptuer Science]. The course is supported by [http://studyitin.ee/ IT Academy].
 ==Evaluation==
@@ 41. rida: / 25. rida: @@
 *61 < score < 70 -- grade 2 (satisfactory)
 *51 < score < 60 -- grade 1 (acceptable)
-score ≤ 50 -- a student has failed to pass
+score ≤ 50 -- a student has failed the course
 =Lectures =
-Lecture slides, necessary files, links and other necessary information would appear here before the lecture or practice.
+Lecture slides, necessary files, links and other necessary information would be provided by means of Moodle (To be set up by 10.02.2017)
-=Lecture 1: Introduction and Decision Trees =
-[[Media:Intro_and_DTrees_ML2016_1.pdf ‎|Slides]]
-==Practice 1==
-[[Media:Practice_1_ML2016.zip ‎|Code and data examples]]
-= Please Observe the practice room change starting 12.02.2016 ICT-402 !!!=
-=Lecture 2: k- Nearest Neighbors  =
-[[Media:Lecture2_ML2016_kNN.pdf |Slides]]
-[http://ciml.info/dl/v0_8/ciml-v0_8-ch02.pdf Reading]
-==Practice 2==
-[[Media:Data_lecture2.zip ‎|Data]]
-=Lecture 3: K- Means  =
-[[Media:Lecture3_ML2016_K_means.pdf |Slides]]
-=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  =
-[[Media:Lecture4_ML2016_Linear_Regression.pdf |Slides]]
-= 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   =
-[[Media:Lecture5_ML2016_GMM_EM_Clusters.pdf |Slides]]
-=Lecture 6: Neural Networks  =
-[[Media:Lecture6_ML2016_Neural_Networks.pdf |Slides]]
-=Lecture 7:  Logistic Regression =
-[[Media:Lecture7_ML2016_Logistic_Regression.pdf |Slides]]
-=Lecture 8:  Competitive learning =
-[[Media:Lecture8_ML2016_Competitive_Learning.pdf |Slides]]
-=Lecture 9:  Markov Models =
-[[Media:Lecture9_ML2016_N_Markov_chains_and_hMm.pdf |Slides]]
-=Lecture 10: Multiclass classification  =
-[[Media:Lecture11_ML2015_N_Multiclass_classification.pdf |Slides]]
-=Lecture 11: Support vector machines=
-[[Media:Lecture11_ML2016_SVM.pdf |Slides]]
-=Lecture 12: Random forests=
-[[Media:Lecture12_ML2016_RandomForests.pdf |Slides]]

Erinevus lehekülje "Machine learning ITI8565 (2017)" redaktsioonide vahel

Redaktsioon: 30. jaanuar 2017, kell 09:23

Evaluation

Lectures

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