Lies, Damn Lies and Machine Learning

---

The idea of giving a machine the ability to think has long captured the imagination of technologists.  However, the efforts to achieve this goal have often over promised and led to disappointment among researchers and those who fund them.  These failures have provided one key lesson:  the machine that “thinks” will first have to be a machine that “learns” from complex data.  In the 1980’s an interdisciplinary field centered on computer science called machine learning emerged with significant differences from its antecedent, artificial intelligence.  Over time the machine learning community has matured and found common purpose with cognitive scientists, physicists, electrical engineers and statisticians to create a fascinating intellectual stew of perspectives, theories, and algorithms that play an important role in fields as diverse as medicine, commerce and national security. 

Given the wide-ranging and sophisticated content of machine learning, understanding the field can be rather daunting.  The goal of this talk is to provide a field guide of sorts to those unfamiliar with machine learning but familiar with some basic mathematical and statistical concepts.  One of the important distinctions is between supervised and unsupervised learning and the role that labels play in learning complex data.  The other important conceptual axis is the Frequentist and Bayesian approaches to how data and models are related.  To access the foundations of machine learning, an emphasis will be placed on how the geometry of representations can provide a unifying perspective across the entire field.  And, finally, some discussion of the latest “breakthrough” in machine learning, Deep Learning, will illustrate how to think about new algorithms and the ultimate goal of creating a thinking machine.

---

---

---

---