Learning objectives

Resources

• Mathematics for Computer Science, Eric Lehman, Tom Leighton, and Albert Meyer 2018 (available online)
• Discrete Mathematics and Its Applications, 7th Edition, Kenneth H. Rosen.
• Extremal Combinatorics with Applications in Computer Science, by Stasys Jukna. (electronically available at our library)
• Graph Theory (online available), by Reinhard Diestel.
• Probability and Computing: Randomized Algorithms and Probabilistic Analysis, by Michael Mitzenmacher and Eli Upfal.
• Randomized Algorithms, by Rajeev Motwani and Prabhakar Raghavan.
• Proofs from the Book, by Martin Aigner, Günter M. Ziegler. (electronically available at our library) Also a translation into Turkish exists.

Similar Courses

• 21-301: Combinatorics at Carnegie Mellon Uni.
• 21-484: Graph Theory at Carnegie Mellon Uni.
• Combinatorics and Graph Theory in Computer Science at Johns Hopkins Uni.
• Applied Extremal Combinatorics at Yale Uni.
• Combinatorics and Discrete Probability at Oxford University.
• Combinatorics and Discrete Probability at Berkeley University.
• 18.409 Topics in Theoretical Comp Sci at M.I.T.

Grading (Tentative)

• Homeworks and Discussion, 30%
• Midterm Exam, 35%
• Final Exam, 35%

Related Conferences

• ACM SIGKDD Conference on Knowledge Discovery & Data Mining (KDD)
• IEEE International Conference on Data Mining (ICDM)
• The Web Conference (WWW)
• The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD)
• European Symposium on Algorithms (ESA)
• International Conference on Approximation Algorithms for Combinatorial Optimization Problems (APPROX)
• International Conference on Randomization and Computation (RANDOM)
• SIAM Symposium on Algorithm Engineering and Experiments (ALENEX)
• International Computing and Combinatorics Conference (COCOON)