Computer Science
Algorithmic Foundations of Data Science
- Course code
- 263-4508-00
- Institution
- ETH Zurich
- Subject
- Computer Science
- Available languages
- EN
Academy
Learning materials
- Cheatsheet
Conceptual Summary
PDF · 6.4 MB
- Lecture notes
Preliminary 1: Linear Algebra & Calculus
PDF · 1.7 MB
- Lecture notes
Preliminary 2: Central Limit Theorem and the Gaussian Distribution
PDF · 1.1 MB
- Lecture notes
Preliminary 3: Concentration bounds
PDF · 1.0 MB
- Lecture notes
Preliminary 4: Convex optimization
PDF · 1.0 MB
- Lecture notes
Lecture note 1: Linear regression and ordinary least squares
PDF · 1.2 MB
- Lecture notes
Lecture note 2: Minimax lower bounds and Bayesian linear regression
PDF · 2.3 MB
- Lecture notes
Lecture note 3: Sparse linear regression and lasso & Huber loss and oblivious outliers
PDF · 1.7 MB
- Lecture notes
Lecture note 4: Low-rank models and statistical guarantees of principal component analysis
PDF · 1.3 MB
- Lecture notes
Lecture note 5: Matrix completion and Bernstein tail bounds for matrices
PDF · 1.1 MB
- Lecture notes
Lecture note 6: Community detection in networks and Grothendieck’s inequality
PDF · 2.3 MB
- Lecture notes
Lecture note 7: Non-negative matrix factorization and topic models
PDF · 1.1 MB
- Lecture notes
Lecture note 8: Tensor decomposition: algorithms and applications (part 1)
PDF · 1.2 MB
- Lecture notes
Lecture note 9: Tensor decomposition: algorithms and applications (part 2)
PDF · 1.4 MB
- Problem set
Lecture note 10: Sum-of-squares for estimation problems
PDF · 1.7 MB
- Lecture notes
Lecture note 11: Clustering and Gaussian mixture models via sum-of-squares
PDF · 1.0 MB
- Lecture notes
Lecture note 12: Robust mean estimation
PDF · 1.2 MB