AI Engineer Math Guide 2026: Which Topics, How Deep, How to Learn?

## 1. Math Depth by Job Type - AI Engineer (LLM/RAG/agent): 30% — intuition sufficient - ML Engineer: 60% — medium depth - Research Scientist: 95% — PhD level ## 2. Five Main Areas Linear Algebra, Calculus, Probability + Statistics, Optimization, Information Theory. ## 3. 6-Month Plan Month 1-2 Linear Algebra (3Blue1Brown + Strang), Month 3 Calculus, Month 4-5 Statistics, Month 6 Optimization + Information. ## 4. Resources 3Blue1Brown (intuition), Karpathy (depth), StatQuest (stats), Khan Academy (practice), Mathematics for ML book (free PDF), BTK Akademi (Turkish free). ## 5. Memorize vs Intuition Don't memorize most formulas — Python does the work. Build intuition for: embeddings, gradient descent, backprop, overfitting, cross-entropy, regularization. ## 6. Conclusion 6 months disciplined math learning sufficient for AI Engineer / ML Engineer. Choose appropriate depth based on career target.