“When you’re fundraising, it’s AI. When you’re hiring, it’s ML. When you’re implementing, it’s logistic regression.”—everyone on Twitter ever. What does Machine Learning (ML) actually mean? The layman explanation is that ML is a field that equips computers with the capability to “learn” from data without being programmed explicitly, though, to be clear, teaching computers to “learn” still requires a bit of programming. Generally speaking, ML aims to create a machine that consumes data and evolve into an intelligent machine that can automatically accomplish complicated tasks. To perform such feats, ML has transformed into a truly multidisciplinary field, borrowing ideas and techniques from Mathematics, Statistics, Computer Science, Game Theory, Physics, Computational Neuroscience, and, inevitably, specific domain knowledge of interest.
In this talk, we will first motivate you with the applications of ML and why you should care about ML. Standard formulations of ML problems will be discussed. We will then review many categories and terminologies of ML, walking through digital age buzzwords such as Deep Learning, Bayesian methods, Likelihood, Regression, etc. Some of these concepts will be discussed in details, including Deep Learning, the successful rebranding of the upgraded artificial neural networks-based learning algorithms that drive modern AI revolution. Lastly, we will touch upon the limitations of classical ML algorithms, and end the talk with open discussions on potential advantages of quantum ML counterparts.