Add Somesh's slides.
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@ -17,7 +17,7 @@ Data Privacy* (AFDP) by Cynthia Dwork and Aaron Roth, available
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9/26 | Privacy for data streams <br> **Paper:** Chan, Shi, and Song. [*Private and Continual Release of Statistics*](https://eprint.iacr.org/2010/076.pdf). | Yinglun
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9/26 | Privacy for data streams <br> **Paper:** Chan, Shi, and Song. [*Private and Continual Release of Statistics*](https://eprint.iacr.org/2010/076.pdf). | Yinglun
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10/1 | Local differential privacy <br> **Paper:** Erlingsson, Pihur, and Korolova. [*RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response*](https://arxiv.org/pdf/1407.6981.pdf). | Justin
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10/1 | Local differential privacy <br> **Paper:** Erlingsson, Pihur, and Korolova. [*RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response*](https://arxiv.org/pdf/1407.6981.pdf). | Justin
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| <center> <h4> **Adversarial Machine Learning** </h4> </center> |
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| <center> <h4> **Adversarial Machine Learning** </h4> </center> |
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10/3 | AML: overview and basics <br> <center> **GUEST LECTURE** </center> | Somesh Jha
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10/3 | [AML: overview and basics](../resources/slides/somesh-aml.pdf) <br> <center> **GUEST LECTURE** </center> | Somesh Jha
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10/8 | History of Adversarial ML <br> **Paper:** Biggio and Roli. [*Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning*](https://arxiv.org/pdf/1712.03141). | Meghana
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10/8 | History of Adversarial ML <br> **Paper:** Biggio and Roli. [*Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning*](https://arxiv.org/pdf/1712.03141). | Meghana
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10/10 | Adversarial examples <br> **Paper:** Szegedy, Zaremba, Sutskever, et al. [*Intriguing Properties of Neural Networks*](https://arxiv.org/pdf/1312.6199.pdf). | Shimaa
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10/10 | Adversarial examples <br> **Paper:** Szegedy, Zaremba, Sutskever, et al. [*Intriguing Properties of Neural Networks*](https://arxiv.org/pdf/1312.6199.pdf). | Shimaa
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10/15 | <center> **NO CLASS: INSTRUCTOR AWAY** </center> |
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10/15 | <center> **NO CLASS: INSTRUCTOR AWAY** </center> |
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