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Section 02 · A. Build the foundation

Maths for AI Engineer Interviews

Linear algebra, calculus, probability and statistics — the theory every ML answer stands on.

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20topics290interview Q&A145practice problems

Topics in this section

Each topic pairs a video with the theory, interview questions & answers, and practice problems whose solutions unlock as you complete them.

02.1
Linear Algebra
18 Q&A9 practice36 min
02.2
Calculus & Optimisation
16 Q&A8 practice32 min
02.3
Probability (the language of uncertainty)
20 Q&A10 practice40 min
02.4
Statistics & Inference (the language of evidence)
20 Q&A10 practice40 min
02.5
The Maths Inside Training
16 Q&A8 practice32 min
02.6
Interview-Favourite Derivations
20 Q&A10 practice32 min
02.7
Information Theory for ML
14 Q&A7 practice28 min
02.8
Numerical Methods & Floating-Point Stability
14 Q&A7 practice28 min
02.9
Matrix Calculus for Backprop
14 Q&A7 practice28 min
02.10
Bayesian Thinking & Inference
14 Q&A7 practice28 min
02.11
Probability Distributions Deep-Dive
14 Q&A7 practice28 min
02.12
Discrete Maths & Graphs for AI
12 Q&A6 practice24 min
02.13
Convex & Constrained Optimization
12 Q&A6 practice24 min
02.14
Optimization Algorithms (the optimizer zoo)
14 Q&A7 practice28 min
02.15
Dimensionality Reduction & Spectral Methods
12 Q&A6 practice24 min
02.16
Sampling & Monte Carlo
12 Q&A6 practice24 min
02.17
Experimentation & Causal Inference
12 Q&A6 practice24 min
02.18
Geometry, Distances & Kernels
12 Q&A6 practice24 min
02.19
Reinforcement Learning Maths (MDPs & Bellman)
12 Q&A6 practice24 min
02.20
Fourier, Convolution & Signal Maths
12 Q&A6 practice24 min