Assessment of Your Teaching Styles and the Technologies You Use
David Singer Visiting Professor
Tue Jan 9, Wed Jan 17, 01-03:00pm, TBA
No limit but advance sign up required (see contact below)
Signup by: 08-Jan-2007
Repeating event. Participants welcome at any session
This two hour session will be offered on two different days. It will be of particular interest to those faculty and instructors who wish to accurately assess the effectiveness of their specific teaching styles and the educational technologies that they use. Part of the session will involve collaboration of those taking part in the development of strategies they might consider employing and educational technologies that they might consider using. An emphasis will be placed on ways to assess whatever learning styles or educational technologies are used.
Contact: David Singer, x3-5759, singerd@mit.edu
Cosponsor: Electrical Engineering and Computer Science
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Developmental Dyslexia: Perceptual Aspects, Diagnosis, Brain-correlates, Remediation and Prevention.
Gadi Geiger, Nadine Gaab
Thu Jan 18, 10-04:00am, 46-3310, class is 10-12 and 2-4 pm
Enrollment limited: first come, first served
Limited to 100 participants.
The first part will describe how dyslexics and typical readers differ in their visual and auditory perception. Based on these findings a diagnostic process and a regimen of practice for learning a new perceptual strategy for reading will be discussed. A study on the prevention of dyslexia will also be presented.
The second part will focus on the neural (brain) correlates of developmental dyslexia and will summarize findings from Electroencephalography (EEG), Magneto-encephalography (MEG) and functional magnetic resonance imaging (fMRI). Furthermore, the much-debated connection between musical training and literacy skills will be discussed
Contact: Gadi Geiger, 46-5155, x3-9646, gadi@AI.MIT.EDU
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From Understanding Cortex to Building Intelligent Machines
Tomaso Poggio, Thomas Serre, Tony Ezzat
Wed Jan 24, 10am-01:00pm, 46-3310
Thu Jan 25, 10am-12:00pm, 46-3310
Enrollment limited: first come, first served
Limited to 100 participants.
Participants requested to attend all sessions (non-series)
A brief introduction to statistical learning theory will be followed by a description of highlights research in computational neuroscience at CBCL. The main thesis of the class is that progress in AI is increasingly dependent on advances in computational neuroscience. In the class we will review recent work on visual and auditory recognition
Contact: Tomaso Poggio, 46-3310, x3-5230, tp@ai.mit.edu
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