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.