Reinforcement learning in sports analytics | AISC

Channel:
Canada LLMs Explained - Aggregate Intellect - AI.SCIENCE
Subscribers:
22,300
Published on ● Video Link: https://www.youtube.com/watch?v=vgivN3fhs9g


Duration: 1:02:27
798 views
27

For slides and more information on the paper, visit https://ai.science/e/reinforcement-learning-in-sports-analytics--2021-01-28

Speaker: Oliver Schulte; Host: Susan Shu Chang

Motivation:
What makes sports analytics difficult, compared to other data analysis problems, and how can machine learning be used to develop new approaches to these challenges?

Oliver Schulte will describe some applications of machine learning techniques in sports analytics, that offer different strengths and weaknesses than more traditional statistical modelling.

The talk focuses on how reinforcement learning addresses a key problem in sports analytics: Predicting the future success of a team given the current state of a match.



Other Videos By LLMs Explained - Aggregate Intellect - AI.SCIENCE


2021-02-25Topological Deep Learning
2021-02-24Explainable AI to Automate Loan Underwriting Decisions | AISC
2021-02-23'Less Than One'-Shot Learning (author speaking)
2021-02-18Machine Learning in Mobile Cybersecurity: An Overview
2021-02-17Author speaking: Proper Machine Learning Explanations through LIME using OptiLIME framework | AISC
2021-02-11Non-Euclidean Universal Approximation | AISC
2021-02-09Explainable AI with Layer-wise Relevance Propagation (LRP)
2021-02-04An Introduction to Quantum Computing
2021-02-04Predicting compound activity from phenotypic profiles
2021-02-03A Survey on the Explainability of Supervised Machine Learning
2021-01-29Reinforcement learning in sports analytics | AISC
2021-01-27We Can Measure XAI Explanations Better with Templates | AISC
2021-01-26Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods | AISC
2021-01-21Deep Learning in Healthcare and Its Practical Limitations
2021-01-14Introduction to NVIDIA NeMo - A Toolkit for Conversational AI | AISC
2021-01-14Explainable Classifiers Using Counterfactual Approach | AISC
2021-01-13Machine learning meets continuous flow chemistry: Automated process optimization | AISC
2021-01-12Screening and analysis of specific language impairment | AISC
2021-01-07High-frequency Component Helps Explain the Generalization of Convolutional Neural Networks | AISC
2021-01-06Locality Guided Neural Networks for Explainable AI | AISC
2021-01-05Explaining image classifiers by removing input features using generative models | AISC