Algorithmics is the systematic study of the design and analysis of algorithms.
This 30 point course covers all the fundamentals of discrete mathematics, data structures, and algorithms that a data scientist requires to do research.
- Prof. Bakh Khoussainov (Lecturer weeks 1-6); Office: Short Street Building 810
- Dr. Michael J. Dinneen (Coordinator/Lecturer weeks 7-12); Office 303-425 [Will try to be in my office during afternoons, when not teaching.]
- Tutor: Roger Su <email@example.com>
- (Class Reps)
Tentative Lecture Schedule:
Week 1: Logic (Boolean formula, predicates/propositions)
Week 2: Proofs (induction, contradiction, adversary arguments)
Week 3: Sets, Functions, and Counting
Week 4: Intro Analysis (big Oh/Omega/Theta, limit rules, properties)
Week 5: Tree/DAG/Graph Abstract Data Types (representations, basic algorithms: BFS/DFS,..)
Week 6: Overflow + Midterm test
Week 7: Greedy algorithms
Week 8: Divide-and-Conquer
Week 9: Dynamic Programming
Week 10: Network Flow (min-cut and max-flow duality)
Week 11: Intro NP-completeness (polynomial-time reductions)
Week 12: Overflow and review
Course Assessment (Updated April 2020):
Assignments at 20% x 2
Midterm test at 20% (Bakh)
Final exam 40% (Michael)
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