Data Structures & Algorithms

Course outline:

• Random Numbers
• Concepts of Algorithms
• Data Structures
• Heaps
• Greedy Algorithms and Dynamic Programming
• Parallel Computing
• P and NP
• Numerical Aspects of Algorithms
• Systems of Linear Equations
• Least Squares
• Eigenvalues and Sparse Matrices
• Systems of Nonlinear Equations
• Numerical Integration and MCMC
• Numerical Optimization

Some online resources I’ve found helpful:

• Steven Skiena https://m.youtube.com/watch?v=A2bFN3MyNDA&list=PLOtl7M3yp-DX32N0fVIyvn7ipWKNGmwpp
• MIT 6.006 https://m.youtube.com/playlist?list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb
• An Open Guide to Data Structures and Algorithms https://pressbooks.palni.org/anopenguidetodatastructuresandalgorithms/
• Algorithms by Jeff Erickson http://jeffe.cs.illinois.edu/teaching/algorithms/#book
• Awesome Algorithms on GitHub https://github.com/tayllan/awesome-algorithms

References

• (Skiena 2020)

Skiena, Steven S. 2020. The Algorithm Design Manual. 3rd ed. Texts in Computer Science. Springer. https://doi.org/10.1007/978-3-030-54256-6.