These slides include many inappropriate graphs. If you want to tell the summary of the data correctly, you should avoid to use graphs in this presentation. They can mislead those who view them.
In English, the title of presentaion is "24 slides including graphs that should not be absolutely drawn".
機械学習の社会実装では、予測精度が高くても、機械学習がブラックボックであるために使うことができないということがよく起きます。
このスライドでは機械学習が不得意な予測結果の根拠を示すために考案されたLIMEの論文を解説します。
Ribeiro, Marco Tulio, Sameer Singh, and Carlos Guestrin. "" Why should i trust you?" Explaining the predictions of any classifier." Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. 2016.
The document describes various probability distributions that can arise from combining Bernoulli random variables. It shows how a binomial distribution emerges from summing Bernoulli random variables, and how Poisson, normal, chi-squared, exponential, gamma, and inverse gamma distributions can approximate the binomial as the number of Bernoulli trials increases. Code examples in R are provided to simulate sampling from these distributions and compare the simulated distributions to their theoretical probability density functions.
機械学習の社会実装では、予測精度が高くても、機械学習がブラックボックであるために使うことができないということがよく起きます。
このスライドでは機械学習が不得意な予測結果の根拠を示すために考案されたLIMEの論文を解説します。
Ribeiro, Marco Tulio, Sameer Singh, and Carlos Guestrin. "" Why should i trust you?" Explaining the predictions of any classifier." Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. 2016.
The document describes various probability distributions that can arise from combining Bernoulli random variables. It shows how a binomial distribution emerges from summing Bernoulli random variables, and how Poisson, normal, chi-squared, exponential, gamma, and inverse gamma distributions can approximate the binomial as the number of Bernoulli trials increases. Code examples in R are provided to simulate sampling from these distributions and compare the simulated distributions to their theoretical probability density functions.
2021年11月18日にResorTech EXPO in Okinawa 2021において実施された伊藤昌毅(東京大学 大学院情報理工学系研究科 准教授)の講演です。
MaaS (Mobility as a Service) というキーワードが一昨年頃より注目され、ITと結びつくことによる公共交通の可能性が改めて注目されている。本講演では、日本や世界で進むMaaSについて概観するとともに、沖縄での可能性について考える。沖縄ではGTFS形式による公共交通オープンデータの整備が進み、データを活用したサービス開発や公共交通の高度化の気運が高まっている。コロナ後を見据え、世界に開けた交通サービスを構築するためのポイントを議論する。
https://resortech-expo.okinawa/program/event04/
Koh Takeuchi, Ryo Nishida, Hisashi Kashima, Masaki Onishi. "Grab the Reins of Crowds: Estimating the Effects of Crowd Movement Guidance Using Causal Inference", AAMAS, 2021.
のスライド
What i think about when i conduct research in the societyMasaki Ito
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1. The document discusses a presentation given by Professor Masaki Ito on his research involving public transportation data.
2. It describes how Professor Ito worked to promote open data standards for public transportation schedules in Japan, including establishing forums for discussion and organizing demonstrations.
3. It explains that open data helped transportation services recover more quickly from natural disasters by providing updated schedule and routing information to citizens when agencies had limited resources.
1) The document describes a bus company in Hokkaido, Japan called Takushoku Bus that has implemented an open data system called "IchigoLoc" using inexpensive off-the-shelf components to provide real-time bus location and delay information.
2) Takushoku Bus worked with a consultant to develop General Transit Feed Specification (GTFS) and GTFS-realtime (GTFS-RT) feeds for its bus routes that are publicly available as open data on its website and Google Maps.
3) Using inexpensive commercial GPS devices, open-source software, and cloud services, Takushoku Bus was able to set up the IchigoLoc system for around $6,