Linear Digressions

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 about how HMMs are used in genetics research.

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 mean? In part one of a special two-parter on HMMs, Katie, Ben, and special guest Francesco explain the basics of HMMs, and some simple applications of them in the real world. This episode sets the stage...

This is another physics-centered podcast, about an ML-backed particle identification tool that we use to figure out what kind of particle caused a particular blob in the detector. But in this case, as in many cases, it looks hard at the outset to use ML because we don't have labeled training data. Monte Carlo to the rescue! Monte Carlo (MC) is fake data that we generate for ourselves, usually foll...

Ever feel like you could randomly assemble words from a certain vocabulary and make semi-coherent Kanye West lyrics? Or technical documentation, imitations of local newscasters, your politically outspoken uncle, etc.? Wonder no more, there's a way to do this exact type of thing: it's called a Markov Chain, and probably the most powerful way to generate made-up data that you can then use for fun an...

Often machine learning discussions center around algorithms, or features, or datasets--this one centers around interpretation, and ethics. Suppose you could use a technology like fMRI to see what regions of a person's brain are active when they ask questions. And also suppose that you could run trials where you watch their brain activity while they lie about some minor issue (say, whether the card...

In 2000, Enron was one of the largest and companies in the world, praised far and wide for its innovations in energy distribution and many other markets. By 2002, it was apparent that many bad apples had been cooking the books, and billions of dollars and thousands of jobs disappeared. In the aftermath, surprisingly, one of the greatest datasets in all of machine learning was born--the Enron email...

Supervised classification is built on the backs of labeled datasets, but a good set of labels can be hard to find. Great data is everywhere, but the corresponding labels can sometimes be really tricky. Take a few examples we've already covered, like lie detection with an MRI machine (have to take pictures of someone's brain while they try to lie, not a trivial task) or automated image captioning (...

So, um... what about machine learning for audio applications? In the course of starting this podcast, we've edited out a lot of "um"'s from our raw audio files. It's gotten now to the point that, when we see the waveform in soundstudio, we can almost identify an "um" by eye. Which makes it an interesting problem for machine learning--is there a way we can train an algorithm to recognize the "um" p...

Now that we know about eigenfaces (if you don't, listen to the previous episode), let's talk about how it breaks down. Variations that are trivial to humans when identifying faces can really mess up computer-driven facial ID--expressions, lighting, and angle are a few. Something that can easily happen is an algorithm can optimize to identify one of those traits, rather than the underlying trait of...

A true classic topic in ML: Facial recognition is very high-dimensional, meaning that each picture can have millions of pixels, each of which can be a single feature. It's computationally expensive to deal with all these features, and invites overfitting problems. PCA (principal components analysis) is a classic dimensionality reduction tool that compresses these many dimensions into the few that...

Baseball is characterized by a high level of equality between teams; even the best teams might only have 55% win percentages (contrast this with college football, where teams go undefeated pretty regularly). In this regime, where 2 outcomes (Giants win/Giants lose) are approximately equally likely, we can model the win/loss chances with a binomial distribution. Using the binomial distribution, we...

Computers are capable of many impressive feats, but making you laugh is usually not one of them. Or could it be? This episode will talk about a custom-built machine learning algorithm that searches through text and writes jokes based on what it finds. The jokes are formulaic: they're all of the form "I like my X like I like my Y: Z" where X and Y are nouns, and Z is an adjective that can describe...

In the 1850s, there were a lot of things we didn’t know yet: how to create an airplane, how to split an atom, or how to control the spread of a common but deadly disease: cholera. When a cholera outbreak in London killed scores of people, a doctor named John Snow used it as a chance to study whether the cause might be very small organisms that were spreading through the water supply (the prevailin...

Machine learning and particle physics go together like peanut butter and jelly--but this is a relatively new development. For many decades, physicists looked through their fairly large datasets using the laws of physics to guide their exploration; that tradition continues today, but as ever-larger datasets get made, machine learning becomes a more tractable way to deal with the deluge. With this i...