This class is a graduate course in machine learning for Sport Analytics.

Prerequisites: A good knowledge of statistics, linear algebra, calculus and machine learning is necessary as well as good programming skills. A good knowledge of computer vision is strongly recommended.

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  • Jan 9th: Course started!
  • Challenges are up
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    When emailing me, please put CSC2541 in the subject line.

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    Each student will need to compete on 2/3 challenges, present once or twice in class (depending on enrollment), participate in class discussions, complete a project (which could be a challenge done individually or in pairs) and do the quizzes.

    The final grade will consist of the following
    Presentation (presentation of papers in class)10%
    Quizzes (solving some sport analytics tasks)25%
    Challenges (competing in 2/3)40%
    Project (final report, presentation)25%


    Depending on enrollment, each student will need to present a few papers in class. The presentation should be clear and practiced and the student should read the assigned paper and related work in enough detail to be able to lead a discussion and answer questions. Extra credit will be given to students who also prepare a simple experimental demo highlighting how the method works in practice. In the presentation, also provide the citation to the papers you present and to any other related work you reference

    Deadline: The presentation should be handed in one day before the class (or before if you want feedback).

    Structure of presentation:
    High-level overview with contributions
    Main motivation
    Clear statement of the problem
    Overview of the technical approach
    Strengths/weaknesses of the approach
    Overview of the experimental evaluation
    Strengths/weaknesses of evaluation
    Discussion: future direction, links to other work


    Projects can be done in pairs or individually. Students can use a challenge as their project, or choose a different topic. The grade will depend on the ideas, how well you present them in the report, how well you position your work in the related literature, how thorough are your experiments and how thoughtful are your conclusions.

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    Coming soon

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    Choose 2 out of the following 3 challenges
    Challenge 1 Detection and trackingDue end of class
    Challenge 2 Unsupervised learning Due end of class
    Challenges 3 BracketologyDue March 11th

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    Jan 9Introduction Raquel Urtasun intro
    Jan 16Intro to Convolutional Nets Bin Yang Conv Nets
    Jan 23Intro to RNNs Mengye Ren RNNs
    Jan 30Intro to Generative Models Shenlong Wang Generative Models
    Feb 6Intro to Field Localization Namdar Homayounfar Field Localization
    Feb 13Intro to Object Detection Bin Yang Object Detection
    Feb 27Player Tracking Global Data Association for Multi-Object Tracking Using Network Flows CVPR 2008 [PDF
    L. Zhang and R. Nevatia

    Multiple Object Tracking using K-Shortest Paths Optimization PAMI 2011 [PDF
    J. Berclaz, F. Fleuret, E. Turetken, and P. Fua

    Multi-target tracking by discrete-continuous energy minimization PAMI 2016 [PDF
    A. Milan, K. Schindler, S. Roth

    Davi Frossard Player Tracking
    Feb 27Play Recognition Classifying NBA Offensive Plays Using Neural Networks MIT SLOAN 2016 [PDF
    K. Wang and R. Zemel

    Jackson Wang Play Recognition
    March 6Play, player and defense Recognition Generating Long-term Trajectories Using Deep Hierarchical Networks NIPS 2016 [PDF
    S. Zheng, Y. Yue and J. Hobbs

    Counterpoints: Advanced Defensive Metrics for NBA Basketball MIT SLOAN 2015 [PDF
    A. Franks, A. Miller, L. Bornn and K. Goldsberry

    Characterizing the spatial structure of defensive skill in professional basketball Annals of Applied Statistics 2015 [PDF
    A. Franks, A. Miller, Luke Bornn and K. Goldsberry

    Predicting Points and Valuing Decisions in Real Time with NBA Optical Tracking Data MIT SLOAN 2014 [PDF
    D. Cervone, A. D'Amourt, L. Bornn and K. Goldsberry

    A Multiresolution Stochastic Process Model for Predicting Basketball Possession Outcomes Journal Of The American Statistical Association 2016 [PDF
    D. Cervone, A. D'Amourt, L. Bornn and K. Goldsberry

    Jackson Wang PPD Recognition
    March 13Ball Tracking Take your Eyes off the Ball: Improving Ball-Tracking by Focusing onTeam Play CVIU 2013 [PDF
    X. Wang, V. Ablavsky, H. Ben Shitrit and P. Fua

    What Players do with the Ball: A Physically Constrained Interaction Modeling Optimization CVPR 2016 [PDF
    A. Maksai, X. Wang and P. Fua

    TBD Ball Tracking
    March 13Activity Recognition Detecting events and key actors in multi-person videos CVPR 2016 [PDF
    V. Ramanathan, J. Huang, S. Abu-El-Haija, A. Gorban, K. Murphy, Li Fei-Fei

    TBD Activity Recognition
    March 20Automatic Filming of Sports Learning Online Smooth Predictors for Realtime Camera Planning using Recurrent Decision Trees CVPR 2016 [PDF
    J. Chen, H. M. Le, P. Carr, Y. Yue and J. Little

    TBD Automatic Filming