Introduction to Machine Learning
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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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bootcamp07_guyon_itml.pdf (566.8 KB)
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Introduction.ppt (2.8 MB)
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Reviews and comments:
Hello Sir,
I am unable to download lectures due to less internet speed at my end.
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Introduction to machine learning by Isabelle guyon...
vaibhav_gandhi@yahoo.com
Thank you
Vaibhav
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
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.
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deca ko deca odraslii nista nisu naucili sve dok se ne pakuje toje romcki dokumentarna igra pesmaje uvjek tu.
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?
Hi,
where can I found the videos (sorted) that follows this lectures?
Thanks
The video is very clear about machine learning. In this video we can learn about major part of machine learning.
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cleared the idea of models and methods in AI and machine learning concepts.
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