Teacher since October 2025
Katarzyna Wolska
Linear algebra for AI, with geometry first and notation second
9
tutors built
4.6Sample
average from 21 reviews
338Sample
lessons taught by their tutors
About Katarzyna
I teach the linear algebra behind modern AI: vectors, matrices, similarity, eigenvectors and the methods built on them, such as PCA, clustering and recommender systems. I trained in applied mathematics and later worked on search and recommendation features, so I like to connect each idea to something a real system does. My lessons begin with pictures and small numbers you can compute by hand, then move to notation once the idea is clear. I also enjoy helping adults who are returning to maths after a long break and want to rebuild confidence without being rushed.
Knows about
Tutors by Katarzyna
9 tutors
Maths Refresher for Returning Adults
Rebuild the school maths you need for data and AI, calmly and at your own pace73 lessonsSampleKatarzyna WolskaFreeReading Maths Notation in AI PapersReading Maths Notation in AI Papers
Translate the symbols in machine learning papers into plain words and small examples55 lessonsSampleKatarzyna Wolska$6Matrix Multiplication IntuitionMatrix Multiplication Intuition
See matrix multiplication as many dot products and as a transformation of space50 lessonsSampleKatarzyna Wolska$5Dimensionality Reduction in PracticeDimensionality Reduction in Practice
Use PCA, t-SNE and UMAP well, and avoid reading too much into pretty plots45 lessonsSampleKatarzyna Wolska$7Vectors and Matrices for AIVectors and Matrices for AI
Understand vectors and matrices as the language AI uses to store and transform data43 lessonsSampleKatarzyna Wolska$4Eigenvalues, Eigenvectors and PCAEigenvalues, Eigenvectors and PCA
Derive principal component analysis from eigenvectors and use it with full understanding39 lessonsSampleKatarzyna Wolska$11Clustering Without GuessworkClustering Without Guesswork
Group data with k-means, hierarchical clustering and DBSCAN, and check the groups are useful33 lessonsSampleKatarzyna Wolska$4Dot Products and SimilarityDot Products and Similarity
Measure how alike two vectors are and see how search and recommendations use itKatarzyna Wolska$4Recommender Systems ExplainedRecommender Systems Explained
Understand how recommendations are made, evaluated and kept from narrowing what people seeKatarzyna Wolska$8Recent reviews
What students said about Katarzyna's tutors.
- Patrick O.Sample
Very patient. It honestly flagged where a paper's notation was ambiguous, which was reassuring.
- Rodrigo P.Sample
Exactly the depth I wanted before reading papers that use spectral methods.
- Ilse V.Sample
Stopped me from presenting t-SNE cluster sizes as meaningful. Running several seeds is now part of my routine.
- Mateusz Z.Sample
Rigorous and clear. Deriving PCA from maximising u transpose C u was the explanation I had been missing for years.
- Aiko F.Sample
The SVD connection lesson was dense but excellent. I would have liked one more worked example on reconstruction error.
- Yan L.Sample
Clear explanation of the difference between t-SNE and UMAP settings. Helpful for visualising our document embeddings.