WPI Worcester Polytechnic Institute

Computer Science Department
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DS504/CS586 - Big Data Analytics - Spring 2020

Version:

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Home Class Info Schedule Projects
Grading Reviews Resources

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Tentative Schedule:

Slides will be updated before each lecture.

+-1. Week 1 (1/16 R):

    Topic 0: Overview of Big Data Analytics (slides) and Class Logistics (Slides)
    Readings: N/A

+-2. Week 2 (1/23 R):

+-3. Week 3 (1/30 R):

    Topic 1: Big data acquisition and measurement (Slides).
    Reading: pp.1-6 Section I - Section III: [ICDE'14]Region Sampling and Estimation of GeoSocial Data with Dynamic Range Calibration. (paper)
    Readings: pp.1-5 Section 1 - Section 3.1 before Remark 1: [TKDE] Efficiently Estimating Statistics of Points of Interests on Maps (paper)
    Topic 2: Big data Preprocessing/Cleaning.(Slides)
    Reading: [ACM SIGSPATIAL GIS 2009] Map-Matching for Low-Sampling-Rate GPS Trajectories. (paper)
    Optional: Section 3.1-3.5 in [ACM TIST] Trajectory Data Mining: An Overview.(paper)
    Note: Individual Project #1 Starts: Online Sampling and Estimation.

-4. Week 4 (2/6 R):
    Topic 3: Big data Management.(Slides)
    Reading1: Section 4.1 in [ACM TIST] Trajectory Data Mining: An Overview.(paper)
    Reading2: [ACM CIKM 2016] Sampling Big Trajectory Data. (paper)
    Topic 4: Big Graph Data Mining I (Sampling Large-Scale Networks via Random Walk). (Slides)
    Readings: M. Gjoka, M. Kurant, C. T. Butts, A. Markopoulou, Walking in Facebook: A Case Study of Unbiased Sampling of OSNs, INFOCOM 2010. (paper)
    Readings: Section 0 and Section 1. L. Lovasz, Random Walks on Graphs: A Survey, Combinatorics, Volume 2, 1993. (paper)

-5. Week 5 (2/13 R):
    Topic 4: Big Graph Data Mining II (Node Importance Ranking on Large-Scale Graphs/Networks)(Slides)
    Readings: E. Even-Dar and A. Shapira, A Note on Maximizing the Spread of Influence in Social Networks, WINE 2007.(paper)
    Readings: PageRank (Link), Hub and Authority (HITS) (Link)
    Topic 5: Deep Learning. (Slides)
    Note: Individual Project #1 Due: Online Sampling and Estimation.
    Note: Individual Project #2 Starts: Classification with Deep Neural Network Model.

-6. Week 6 (2/20 R):
    Topic 5: More on Deep Learning. (Slides)

-7. Week 7 (2/27 R): No Class. WPI Reading day. (See this link)

-8. Week 8 (3/5 R):
    Topic 6: Generative Adversarial Networks (GANs). (Slides)
    Readings: A Beginner's Guide to Generative Adversarial Networks (GANs). (Link)
    Readings: Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S.,Bengio, Y. (2014). Generative adversarial nets. In Advances in neural information processing systems (pp. 2672-2680). (paper)
    Note: Individual Project #2 Due: Classification with Deep Neural Network Model.
    Note: Individual Project #3 Starts: GAN.
    Note: Team Project Starts.

-9. Week 9 (3/12 R): No Class. Spring Break. (See this link)

-10. Week 10 (3/19 R): No Class. Delayed resumption of WPI class (due to COVID-19). (See this link)

-11. Week 11 (3/26 R):
    Topic 7: Flow-based Generative Models. (Slides) (Updated on 3/27!)
    Readings: Flow-based Deep Generative Models. (Blog)
    Note: Team Project Proposal Due. Submit 2-page proposal to Canvas discussion board.
-12. Week 12 (4/2 R):
    Topic 8: Meta Learning and Few Shot Learning (I). (Slides) (Updated on 04/03!).
    Readings: Meta Learning tutorial. (Link)
    Readings: Chelsea Finn, Pieter Abbeel, Sergey Levine, Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. (PDF)
    Readings: Alex Nichol and Joshua Achiam and John Schulman, On First-Order Meta-Learning Algorithms. (PDF)
    Note: Individual Project #3 Due: GAN.
    Note: Individual Project #4 Starts: Meta-Learning and Few Shot Learning.

-13. Week 13 (4/9 R):
    Topic 8: Meta Learning and Few Shot Learning (II). (You can find opening slides here and content Slides from the link above in Week 12!)
    Readings: [Siamese Networks] Chopra, S.; Hadsell, R.; LeCun, Y. (June 2005). "Learning a similarity metric discriminatively, with application to face verification". 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05). (PDF)
    Readings: [Prototypical Networks] Jake Snell, Kevin Swersky, Richard S. Zemel, Prototypical Networks for Few-shot Leaning. (PDF)

-14. Week 14 (4/16 R):
    Topic 9: Adversarial Attacks/Defense. (Slides)
    Readings: [FGSM] Ian J. Goodfellow, Jonathon Shlens and Christian Szegedy, EXPLAINING AND HARNESSING ADVERSARIAL EXAMPLES, ICLR 2015(PDF).
    Readings: Mahmood Sharif, Sruti Bhagavatula, Lujo Bauer, Michael K. Reiter, Accessorize to a Crime: Real and Stealthy Attacks on State-of-the-Art Face Recognition, CCS 2016(PDF).

-15. Week 15 (4/23 R):
    Topic 10: Explainable AI (XAI) (Slides).
    Readings: XAI Tutorial in KDD 2019 conferenceLink.
    Readings: Understanding Neural Networks Through Deep Visualization. (PDF).
    Note: Team Project Progressive Report Due. Submit 5-page progressive report to Canvas discussion board.

-16. Week 16 (4/30 R):
    Topic 11: Deep Nerual Network Compression and Class Review(Slides).
    Readings: Jonathan Frankle, Michael Carbin, THE LOTTERY TICKET HYPOTHESIS:FINDING SPARSE, TRAINABLE NEURAL NETWORKS. (PDF).
    Readings: Geoffrey Hinton, Oriol Vinyals, Jeff Dean, Distilling the Knowledge in a Neural Network. (PDF).
    Note: Individual Project #4 Due: Meta-Learning and Few Shot Learning.

-17. Week 17 (5/7 R):
    Team Project Presentations/Posters
    All teams.
    Note: Team Project Due. Submit your team report and individual self-and-peer evaluation form on Canvas.

To be updated.

yli15 at wpi.edu