# GLM or Statistical Inference for Machine Learning?

First of all: I don't know if this is the correct place to ask this question. If it is not, please help me deciding where I should post it.

I want to study Machine Learning as a MSc (UCL if possible) and my background is undergraduate mathematics. I'm in my second year and in my third year I can choose only one of the following two statistic modules:

• Statistical Inference(Point Estimation: MLE, MVUE. Hypothesis Testing: Neyman-Pearson Lemma, Likelihood ratio test. Computational inference: MLE, Resampling methods. Bayesian Inference: prior proba, post prob, predictive inference. Decision Based Inference)
• Generalized Linear Model (syllabus here)

what is the best choice? Which one is more useful in ML?

edit The courses I've done so far are:

Y1: Calculus, Multivariable Calculus, Linear Algebra 1, Linear Algebra 2, Mathematical Modelling, Intro Stats and Probability, Differential Equations, Number Theory&Cryptography.

Y2: Statistical Distribution Theory, Intro Applied Mathematics, Analysis, Vector Calculus&Complex Variables, Statistical Methods 1 (linear regression), Applications of vector calculus (tensor, fluidodynamics, electromagnetism, etc), Algorithms (trees, flows), Parial Differential Equations.

• What is otherwise your interests? Which other courses did you take? Mar 25, 2017 at 14:57
• @kjetilbhalvorsen I've edited my question adding the courses that I've taken! Mar 25, 2017 at 15:08
• Both not very helpful for traditional machine learning. Mar 25, 2017 at 15:50
• @StudentT see that's the problem. There is so much contraddictory information on the web. For example a lot of posts say that statistics and maths in general are essential and the most part of ML. But then others say it's not. So why this difference? Is stats used in ML? Mar 25, 2017 at 20:12
• Sorry to add to the confusion, but i think both those are essential for machine learning :) Mar 25, 2017 at 20:34