Interview with Tom Mitchell
interviewer: Davor Orlič,
Knowledge 4 All Foundation Ltd.
interviewee: Tom Mitchell, Carnegie Mellon University
published: Feb. 25, 2007, recorded: September 2006, views: 73865
interviewee: Tom Mitchell, Carnegie Mellon University
published: Feb. 25, 2007, recorded: September 2006, views: 73865
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Description
Tom Mitchell is the first Chair of Department of the first Machine Learning Department in the World, based at Carnegie Mellon. The Videolectures.Net team spoke to him in Pittsburgh at CMU where we discussed about how he started the department, what was the response of the broader community and its past, present and future. "The university said you can only have a department if you have a discipline that is going to be here in one hundred years otherwise you can not have a department."
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Reviews and comments:
you have the same name as me... lmao!
Dr. Mitchell, I wonder if you are the person who appeared on 60 Minutes this evening (Sunday, Jan. 4) talking about work on thought identification. I'm very interested in this subject, as I'm a psychologist and am frequently in the position of trying to determine if someone is being truthful with me.
Thank you,
Judy Okawa
That’s wonderful!
I am an undergraduate student in China; I love this area very much. Indeed, my major is computer software. Machine Learning is a complex theory, like psychology. It not only includes AI algorithm, but also contain theory of brain, neurology, recognizing technique, even psychology. However, the direction in machine learning has been misleading, at least in China. Too many researchers emphasize on comprehension of human language or literature which may extreme hard to make an accurate data. Maybe we should put our attention down to those simple communications, like learning process on birds or cold blood animal. A highly developed science always comes from simple. To directly study human’s complex communicational skill is too fast for us. No one can build two-storey house without the first floor.
I am a beginner for ML. I didnt know it existed before I found out that my problem could only be solved using statistical and learning mechanism.
I found what is called Machine Learning in the internet. Thanks God! I was relieved. But I had this question all the time. The area looks very challenging and narrow. Am I alone? Or are thre some people around the world who can direct me somewhere?...I think this interview helps a lot. Thank you Michell.
Thanks a lot for sharing. Been playing around with machine learning both professionally and as a hobby.
One of my projects ( http://www.mynext.co.uk ) is using simple genetic algorithms to teach itself what each product type is.
Thanks first time found the videolectures.net site, think going to be here all day =]
I read your book about machine learning and it provided me with a robust knowledge in the field of ML.. Best wishes for you Dr.
Thank you for your wonderful book. Is there the chance that somewhen an updated version with a chapter about SVMs and unsupervised learning algorithms??
I've been always looking for a field or major that collides popular subjects(biology, psychology, business and so on) to create something totally amazing. There it is, Machine Learning. Enjoyed the interview.
Thank you Mr. Mitchel for this video.
I was doing some research into your background because my lecturer advised your book for his course at the University of the Witwatersrand.
Your explanations are clear on the objectives of Machine Learning as a field of study.
Kind Regards
Tino
I rewatched the video, very interesting thank you !
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I love this area very much. Indeed, my major is computer software. Machine Learning is a complex theory, like psychology.
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Too many researchers emphasize on comprehension of human language or literature which may extreme hard to make an accurate data. Maybe we should put our attention down to those simple communications, like learning process on birds or cold blood animal.
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Hello, Mr. Michell. It was a great interview! There is no doubt the science of advancing machine learning algorithms through research is difficult. It requires creativity and experimentation. Thanks for enlightening us!
Keep safe.
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Maybe we should put our attention down to those simple communications, like learning process on birds or cold blood animal. A highly developed science always comes from simple. To directly study human’s complex communicational skill is too fast for us.
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