Erinevus lehekülje "Machine learning" redaktsioonide vahel
| 79. rida: | 79. rida: | ||
[[Meedia:Lecture9.pdf|Slides]] | [[Meedia:Lecture9.pdf|Slides]] | ||
| − | [http://see.stanford.edu/materials/aimlcs229/cs229-notes2.pdf Reading, lecture notes by Andrew Ng] | + | [http://see.stanford.edu/materials/aimlcs229/cs229-notes2.pdf Reading, section 2, lecture notes by Andrew Ng] |
== Additional links == | == Additional links == | ||
Redaktsioon: 9. aprill 2014, kell 14:35
Spring 2013/2014
ITI8565: Machine learning
Taught by: Kairit Sirts
EAP: 6.0
Time and place: Fridays
Lectures: 16:00-17:30 X-406 Labs: 17:45-19:15 X-412
Additional information: sirts@ioc.ee, juhan.ernits@ttu.ee
Skype: kairit.sirts
The course is organised by the Department of Comptuer Science. The course is supported by IT Academy.
Students should also subscribe to machine learning list. This is used to spread information about the course in this semester as well as any other machine learning related event happening in TUT (also in future).
New!!! Homework rankings based on results (just for fun): Ranking
This will be updated as the homework results are checked. Stay in tune!
Lecture 1: Introduction, decision trees
Example made in class - When to play tennis?
Reading - contains also the full algorithm for decision tree learning with divide-and-conquer strategy.
First homework is open in moodle. For submitting you have to register to the course
Lecture 2: K nearest neighbours
Lecture 3: K-means clustering, MLE principle
Lecture 4: Gaussian Mixture Model, EM algorithm
Second homework is open in moodle.
Lecture 5: History of neural networks, perceptron
Lecture 6: Artificial neural networks
Third homework is open in moodle.
Data for the third homework
Lecture 7: Linear regresssion
Lecture 8: Logistic regresssion
Lecture 8: Naive Bayes, maximum entropy model
Reading, section 2, lecture notes by Andrew Ng