Personally I'd advise against both SVM's and Bayesian methods for a beginner. Bayesian statistics is very much the deep end of the pool. Graphical models and Bayesian methods generally may make a comeback but such approaches have been superseded by other methods for good reasons, i.e. scaling.
A strong basis in statistics is certainly a great thing, but that can be maximum likelihood plus Bayes law (i.e. "MAP" estimation which is more of a hack to ML than an actual Bayesian method) and provide the big picture for almost everything.
Meanwhile a strong basis in "deterministic methods", as an alternative way to spend that learning effort, has its own rewards. The training algorithms for deep learning are also the hottest algorithm research area in machine learning, and are certainly applicable beyond deep learning. For that matter a thorough understanding of SVM delves into convex optimization, an extremely powerful framework as well.
A strong basis in statistics is certainly a great thing, but that can be maximum likelihood plus Bayes law (i.e. "MAP" estimation which is more of a hack to ML than an actual Bayesian method) and provide the big picture for almost everything.
Meanwhile a strong basis in "deterministic methods", as an alternative way to spend that learning effort, has its own rewards. The training algorithms for deep learning are also the hottest algorithm research area in machine learning, and are certainly applicable beyond deep learning. For that matter a thorough understanding of SVM delves into convex optimization, an extremely powerful framework as well.