I’m currently doing an Master in Economics with a focus on Econometrics. I’ve taken Calc 1-3, Linear Algebra, Real Analysis. Though I haven’t taken a formal Probability theory/mathematical statistics class, I do use them implicitly in my graduate level Econometrics course.

I would greatly appreciate advice on which of the following courses I should take to transition into ML research:

1. Measure theory: Probability spaces and probability measures. Random variables. Expectation and integration. Convergence of random variables. Conditional expectation. The Radon-Nikodym Theorem. Martingales. Stochastic processes. Brownian motion. The Itô integral
2. Stochastic Processes: The course examines Martingales, Poisson Processes, Brownian motion, stochastic differential equations and diffusion processes.
3. Time series: autocorrelation; stationarity; causality;  basic time series models: AR, MA, ARMA; ARCH and GARCH models for financial time series; trend removal and seasonal adjustment; invertibility; spectral analysis; estimation; forecasting. We will also discuss nonstationarity and multivariate time series.

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Ps I can also take ML courses but I thought these courses would allow me to build a stronger foundation.

Not sure which areas of ML I would specialise in but it’ll likely be Causal ML given my Economics background

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