That's "Dr Katie" to You
Katie successfully defended her thesis! We celebrate her return, and talk a bit about what getting a PhD in Physics is like.
14 Mai 20153min
Neural Nets (Part 2)
In the last episode, we zipped through neural nets and got a quick idea of how they work and why they can be so powerful. Here’s the real payoff of that work: In this episode, we’ll talk about a bran...
11 Mai 201510min
Neural Nets (Part 1)
There is no known learning algorithm that is more flexible and powerful than the human brain. That's quite inspirational, if you think about it--to level up machine learning, maybe we should be going ...
1 Mai 20159min
Inferring Authorship (Part 2)
Now that we’re up to speed on the classic author ID problem (who wrote the unsigned Federalist Papers?), we move onto a couple more contemporary examples. First, J.K. Rowling was famously outed usin...
28 Apr 201514min
Inferring Authorship (Part 1)
This episode is inspired by one of our projects for Intro to Machine Learning: given a writing sample, can you use machine learning to identify who wrote it? Turns out that the answer is yes, a person...
16 Apr 20158min
Statistical Mistakes and the Challenger Disaster
After the Challenger exploded in 1986, killing all 7 astronauts aboard, an investigation into the cause was immediately launched. In the cold temperatures the night before the launch, the o-rings th...
6 Apr 201513min
Genetics and Um Detection (HMM Part 2)
In part two of our series on Hidden Markov Models (HMMs), we talk to Katie and special guest Francesco about more useful and novel applications of HMMs. We revisit Katie's "Um Detector," and hear abou...
25 Mar 201514min
Introducing Hidden Markov Models (HMM Part 1)
Wikipedia says, "A hidden Markov model (HMM) is a statistical Markov model in which the system being modeled is assumed to be a Markov process with unobserved (hidden) states." What does that even mea...
24 Mar 201514min
