Introduction to Machine Learning

author: Isabelle Guyon, Clopinet
published: July 2, 2007,   recorded: July 2007,   views: 14390
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Description

This course covers feature selection fundamentals and applications. The students will first be reminded of the basics of machine learning algorithms and the problem of overfitting avoidance. In the wrapper setting, feature selection will be introduced as a special case of the model selection problem. Methods to derive principled feature selection algorithms will be reviewed as well as heuristic method, which work well in practice. One class will be devoted to feature construction techniques. Finally, a lecture will be devoted to the connections between feature section and causal discovery. The class will be accompanied by several lab sessions. The course will be attractive to students who like playing with data and want to learn practical data analysis techniques. The instructor has ten years of experience with consulting for startup companies in the US in pattern recognition and machine learning. Datasets from a variety of application domains will be made available: handwriting recognition, medical diagnosis, drug discovery, text classification, ecology, marketing.

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Download slides icon Download slides: bootcamp07_guyon_itml.pdf (566.8 KB)

Download slides icon Download slides: Introduction.ppt (2.8 MB)


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Reviews and comments:

Comment1 Vaibhav, June 27, 2008 at 8:41 a.m.:

Hello Sir,

I am unable to download lectures due to less internet speed at my end.
Also, electric fluctuations dont help me as the download begins from the start everytime there is an electric fluctuation.
Could you please provide me the link for download so that i can use DAP or REGT for download.. so that even if supply goes OFF, I can continue my download from where I left.
Introduction to machine learning by Isabelle guyon...
vaibhav_gandhi@yahoo.com

Thank you

Vaibhav


Comment2 santana dutta, September 11, 2008 at 3:03 p.m.:

dear sir
I am un able to know What is Machine Learning? What for?it's Applications.so can u plz help me out by giving it on my mail i.d
santana.dutta@yahoo.com


Comment3 oyewole Stanley, January 13, 2009 at 4:31 p.m.:

Kindly tell me more about machine learning. Which programming language will best be use to implement machine learning related task? Im highly interested in it where and How do i start.


Comment4 Dumbasstra Pradeep, June 8, 2009 at 11:14 p.m.:

Hello please to am knowing how am to make intelligent postings on subject machines learnings? I have many chance to abusing your knowledge because of have am too lazy to making the learn.

Also, nuclear proton disturbancing have reduce my internet device throughspeed and i have no enough hertz. Please I have write my email URIL here for send me the fyle.


Comment5 japo duric, March 3, 2012 at 9:24 p.m.:

deca ko deca odraslii nista nisu naucili sve dok se ne pakuje toje romcki dokumentarna igra pesmaje uvjek tu.


Comment6 Parimala R, April 21, 2012 at 9:28 a.m.:

Method Average Maximum

#f Acc #f Acc
original 57 93.03 57 93.15
m1 21 90.70 23 92.50
m2 21 90.41 28 91.00
m3 22 91.13 25 94.26
m4 37 91.76 44 93.41
m5 42 92.72 44 93.70
m6 34 91.77 39 92.50
m7 20 90.13 24 91.13
m8 16 89.43 18 89.75
I have conducted 10 experiments and obtained the above result. What can I conclude? Which method is best?


Comment7 Sinek, February 27, 2013 at 11:49 a.m.:

Hi,

where can I found the videos (sorted) that follows this lectures?

Thanks


Comment8 yaswanth krishna chakka, July 14, 2016 at 10:17 p.m.:

The video is very clear about machine learning. In this video we can learn about major part of machine learning.

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