Tag: classification
Naive Bayes Categorisation (with some help from Elasticsearch)
Back in November, I gave a talk during one of the Friday Hackers and Painters sessions at Plug-in@Block 71, aptly titled “How I do categorisation and some naive bayes sh*t” by Calvin Cheng. I promised I’d write a follow-up blog post with the materials I presented during the talk, so here it is.
Tag: elasticsearch
Naive Bayes Categorisation (with some help from Elasticsearch)
Back in November, I gave a talk during one of the Friday Hackers and Painters sessions at Plug-in@Block 71, aptly titled “How I do categorisation and some naive bayes sh*t” by Calvin Cheng. I promised I’d write a follow-up blog post with the materials I presented during the talk, so here it is.
Tag: implementation
Naive Bayes Categorisation (with some help from Elasticsearch)
Back in November, I gave a talk during one of the Friday Hackers and Painters sessions at Plug-in@Block 71, aptly titled “How I do categorisation and some naive bayes sh*t” by Calvin Cheng. I promised I’d write a follow-up blog post with the materials I presented during the talk, so here it is.
Tag: lspi
Learning about reinforcement learning, with Tetris
For our final assignment for the NUS Introduction to Artificial Intelligence class (CS3243), we were asked to design a Tetris playing agent. The goal of the assignment was to get students to be familiar with the idea of heuristics and how they work, getting them to manually tune features to get a reasonably intelligent agent. However, the professor included this in the assignment folder, which made me think we had to implement the Least-squares Policy Iteration algorithm for the task.
I’ll probably discuss LSPI in more detail in another post, but for now, here are the useful features we found for anyone trying to do the same thing.
Tag: reinforcement-learning
Learning about reinforcement learning, with Tetris
For our final assignment for the NUS Introduction to Artificial Intelligence class (CS3243), we were asked to design a Tetris playing agent. The goal of the assignment was to get students to be familiar with the idea of heuristics and how they work, getting them to manually tune features to get a reasonably intelligent agent. However, the professor included this in the assignment folder, which made me think we had to implement the Least-squares Policy Iteration algorithm for the task.
I’ll probably discuss LSPI in more detail in another post, but for now, here are the useful features we found for anyone trying to do the same thing.
Tag: semantics3
Naive Bayes Categorisation (with some help from Elasticsearch)
Back in November, I gave a talk during one of the Friday Hackers and Painters sessions at Plug-in@Block 71, aptly titled “How I do categorisation and some naive bayes sh*t” by Calvin Cheng. I promised I’d write a follow-up blog post with the materials I presented during the talk, so here it is.
Tag: tetris
Learning about reinforcement learning, with Tetris
For our final assignment for the NUS Introduction to Artificial Intelligence class (CS3243), we were asked to design a Tetris playing agent. The goal of the assignment was to get students to be familiar with the idea of heuristics and how they work, getting them to manually tune features to get a reasonably intelligent agent. However, the professor included this in the assignment folder, which made me think we had to implement the Least-squares Policy Iteration algorithm for the task.
I’ll probably discuss LSPI in more detail in another post, but for now, here are the useful features we found for anyone trying to do the same thing.