machine-learning - Vik's Blog

Programming instrumental music from scratch

I recently posted about automatically making music. The algorithm that I made pulled out interesting sequences of music from existing songs and remixed them. While this worked reasonably well, it also didn’t have full control over the basics of the music; it wasn’t actually specifying which instruments to

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Do the Simpsons characters like each other?

One day, while I was walking around Cambridge, I had a random thought – how do the characters on the Simpsons feel about each other? It doesn’t take long to figure out how Homer feels about Flanders (hint: he doesn’t always like him), or how Burns feels about everyone,

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Using the power of sound to figure out which Simpsons character is speaking

Update: you can find the next post in this series here. In a previous post, I looked at transcripts of Simpsons episodes and tried to figure out which character was speaking which line. This worked decently, but it wasn’t great. It gave us memorable scenes like this one: Homer

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Figuring out which Simpsons character is speaking

Update: you can find the next post in this series here. You probably have a favorite Simpsons character. Maybe you hope to someday block out the sun, Mr. Burns style, maybe you enjoy Homer’s skill in averting meltdowns, or maybe you identify with Lisa’s struggles for acceptance. Through

math

Find the determinant of a matrix

The determinant of a matrix is a number associated with a square (nxn) matrix. The determinant can tell us if columns are linearly correlated, if a system has any nonzero solutions, and if a matrix is invertible. See the wikipedia entry for more details on this. Computing a determinant is

math

Linear Regression from the Ground Up

Linear regression is a very basic technique that we use a lot in machine learning. In a lot of cases (and I have been guilty of this), we just use it without much thought as to how the internals actually work. In a 2-D coordinate system, we can plot observations

math

Inverting your very own matrix

IntroductionI had my natural predilection towards math crushed out of me at some point in school, and after that point, Math (yes, we are referring to the higher power of math) and I had a wary understanding. I dabbled quietly, and Math turned a blind eye to me ignoring some

ml

Predicting season records for NFL teams - part 2

This is the second, technical, part of this series. See the first part for the overview. IntroductionThis post will introduce the technical details behind the nfl season record prediction that was introduced in part one. After selecting the error metric and defining an acceptable baseline, which was setup in part

machine-learning

Predicting season records for NFL teams - overview

This is the first, non-technical, part of this series. See the second part for more detail. IntroductionI was recently looking for a good machine learning task to try out, and I thought that doing something NFL-related would be interesting, because the NFL season is about to start (finally!). Why was

machine-learning

NBA Playoffs Update 5 (5-4)

This is the sixth post in my series on predicting the NBA playoffs with an algorithm. After the Boston loss in their last game, the algorithm is now 5-4 in the playoffs. Hopefully it is correct tonight! Open Sourcing the Code I have had a couple of requests to open

machine-learning

NBA Playoff Predictions Update 4 (5-3)

This is update 4 to my original post about predicting the NBA playoffs with R. With the Thunder beating the Spurs and the Heat losing to the Celtics, the algorithm went 1-1 on predictions, making it 5-3 so far. Making some improvements I have been posting for some time about

machine-learning

NBA Playoff Predictions Update 3 (4-2)

This is my third update to my original post on predicting the NBA playoffs with an algorithm. Here are updates 1 and 2. The algorithm correctly predicted a Boston win, but missed on the Spurs/Thunder game, so it is currently 4-2. Haven’t had any time to update yet,

machine-learning

NBA Playoff Predictions Update 2 and Results (3-1)

This is my second follow-up to my previous two posts which were about predicting NBA games with an algorithm, and my first update to the algorithm. The algorithm’s record is now 3-1, as it correctly predicted Boston and Oklahoma City as winners of their past games. Upcoming things to

machine-learning

Predicting NBA Playoff Games - Results and Update 1

Game Results I recently made a post about developing an algorithm to predict the NBA playoffs, and I concluded with 2 predictions. Although Miami beat the Celtics to make my algorithm 1-0 in terms of predictions, it fell to 1-1 when the Thunder beat the Spurs. So, we are now

machine-learning

Intro to Ensemble Learning in R

Introduction This post incorporates parts of yesterday’s post about bagging. If you are unfamiliar with bagging, I suggest that you read it before continuing with this article. I would like to give a basic overview of ensemble learning. Ensemble learning involves combining multiple predictions derived by different techniques in

machine-learning

Resources for Learning Statistics and Data Mining

There are an abundance of resources on the web to help novices teach themselves statistics and machine learning, but they can be hard to track down. Below are a few resources that have helped me in the past. 1. Khan Academy has a series of excellent videos on the basics