ActuarialExam SRMBasics of Statistical Learning
Exam SRM topic · 7–13% of exam

Basics of Statistical Learning

Supervised and unsupervised learning, model assessment, bias-variance tradeoff, and resampling methods including cross-validation.

What you need to know

These are the key learning objectives for Basics of Statistical Learning on SOA Exam SRM. Paraphrased from the public SOA syllabus — we recommend also checking the current syllabus on soa.org before your exam sitting.

Distinguish between supervised and unsupervised learning tasks

Explain the bias-variance tradeoff and its implications for model selection

Apply resampling techniques such as k-fold cross-validation and the bootstrap

How exclam.ai helps you master Basics of Statistical Learning

Flashcards from your manual

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FSRS spaced repetition

Because Basics of Statistical Learning is 7–13% of your exam, losing it during review costs you. FSRS brings it back at the optimal moment.

Basics of Statistical Learning in the Exam SRM context

SOA Exam SRM has 5 topic areas. Basics of Statistical Learning is weighted at approximately 7–13% of the exam — here is where it sits relative to the other topics.

Topic areaWeight
→ Basics of Statistical Learning7–13%
Linear Models40–50%
Decision Trees20–25%
Principal Components and Cluster Analysis5–10%
Time Series Models5–10%

Other Exam SRM topics

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