Update README.md

README.md

@@ -7,4 +7,4 @@ This repository contains lectures and homeworks for Numerical linear algebra cou
 |1| General info [[GitHub](lectures/general_info.ipynb)] <br> Lecture 1. Floating point arithmetic, vector norms [[GitHub](./lectures/lecture1/lecture-1.ipynb)] <br> Lecture 2. Matrix norms and unitary matrices [[GitHub](./lectures/lecture2/lecture-2.ipynb)] <br> Lecture 3. Matvecs and matmuls, memory hierarchy, Strassen algorithm [[GitHub](./lectures/lecture3/lecture-3.ipynb)] | [Brief Python intro](./after_lectures/3/brief_python_intro.ipynb) <br> [JAX intro](./after_lectures/3/jax_autodiff_tutorial.ipynb) | [Home assignment 1](https://github.com/oseledets/nla2020/blob/main/hw/hw1/hw1.ipynb) <br> Deadline: November, 8, 23:59 MSK |
 | 2 | Lecture 4. Matrix rank, low-rank approximation, SVD [[GitHub](./lectures/lecture4/lecture-4.ipynb)] <br> Lecture 5. Linear systems [[Github](./lectures/lecture5/lecture-5.ipynb)] <br> Lecture 6. Eigenvalues and eigenvectors [[GitHub](./lectures/lecture6/lecture-6.ipynb)] | [PyTorch intro](./after_lectures/4/pytorch_intro.ipynb) | | 
 | 3 | Lecture 7. Matrix decompositions and how we compute them [[GitHub](./lectures/lecture7/lecture-7.ipynb)] <br> Lecture 8. Symmetric eigenvalue problem and SVD [[GitHub](./lectures/lecture8/lecture-8.ipynb)] <br> Lecture 9. From dense to sparse linear algebra [[GitHub](./lectures/lecture9/lecture-9.ipynb)] | | [Home assignment 2](https://github.com/oseledets/nla2020/blob/main/hw/hw2/hw2.ipynb) <br> Deadline: November, 23, 23:59 MSK | |
-| 4 | Lecture 10. Sparse direct solvers [[GitHub](./lectures/lecture10/lecture-10.ipynb)] | | | |
+| 4 | Lecture 10. Sparse direct solvers [[GitHub](./lectures/lecture10/lecture-10.ipynb)] <br> Lecture 11. Intro to iterative methods [[GitHub](./lectures/lecture11/lecture-11.ipynb)] | | | |