[Note: some of these topics (and many of these links) will change, reflecting updates, improvements, and adaptation to the interests of this year's students.]
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| 1. | 9/14++ | Overview & organization Mathematical background |
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Placement test, to be completed in class. If you miss the first class, please fill this out and return to Laurel Sweeney at IRCS. Instructions: there are 20 questions whose answers should be obvious if you know and remember the material, and impossible otherwise. Don't spend more than 10 minutes on it. If you don't know the answer to a question, leave it blank rather than guessing.We don't expect you to know all this materials, or even a majority of it. The point is to give us a sense of who needs (or doesn't need) help with what.
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| 2a | 9/21 | Lab session | Ed Neuman's Matlab tutorials #1, #2, #3 | Problem set #1 |
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| 2b | 9/24 | Lecture: Linear algebra review | (Same readings as lecture 1) | |||||||
| 3a | Color vision as a 3D subspace | |||||||||
| 3b | Lab session | Problem Set #2 Hints for Problem Set #2 |
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| 4a | Paper Discussion | Henrietta Cedergren and David Sankoff, "Variable Rules: Performance as a Statistical Reflection of Competence", Language, 50(2) pp. 333-355, 1974 Background discussion. |
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| 4b | Lecture: Regression | |||||||||
| 5a | Lab session | Problem set #3 |
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| 5b | Lecture: Basic probability and statistics review | Joint probability, conditional probability, and Bayes' Theorem |
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| 6a | Lab session |
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| 6b | Paper discussion | Michael Heilman et al., "An Analysis of Statistical Models and Features for Reading Difficulty Predction", The Third Workshop on Innovative Use of NLP for Building Educational Applications, ACL-08 (link to full proceedings) |
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| 7a | Lecture:
Subspace Methods |
Introduction to Subspace Methods | ||||||||
| 7b | Lab session | Problem set #4 | ||||||||
| 8a | Lecture: Linear classifiers | Classifying multivariate data | ||||||||
| 8b | Paper discussion | Francisco Pereira et al., "Machine learning classifiers and fMRI: a tutorial overview" |
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| 9 | Lecture: Linear shift-invariant systems | Problem set #5 | ||||||||
| 10a | Lab session | |||||||||
| 10b | Lecture: The discrete fourier transform | Towards the Discrete Fourier Transform |
Problem set #6 | |||||||
| 11 | Lab session | |||||||||
| 12a | Lecture | FIR & IIR Filters Poles, zeros and the z-transform |
Problem set #7 | |||||||
| 12b | Paper discussion | G. Kochanski et al., "Loudness predicts prominence: Fundamental frequency lends little", JASA 118(2) 1038-1054 (2005). | ||||||||
| 13a | Lecture: fMRI analysis | |||||||||
| 13b | Paper discussion | TBA |