June 26, 2006
Everything has been graded, so the total percentage on Blackboard is your final grade, minus the .5 percent per early project. So you can add .5 * the number of early projects (listed on Blackboard as well) to your total percentage to get the percentage of your final grade. You can then match up the percentage to the values listed on the syllabus to get what should be your final letter grade, unless Tonga decides to slide the scale any (I think it only goes lower, so you should at least get the grade on the syllabus, if not better).
Thanks for being such great students, it has been fun being your TA. Good luck in the future!
June 19, 2006
project 7 FYI
I have recieved project 7 from most of you (including those sent to the email@example.com email account). However, I'm waiting for a clarification about the answer to the problem, so it probably won't be graded until I get that clarification (hopefully tomorrow).
Homework Keys 13-15
Final Exam Study Guide
Here is a list of things you might want to study in order to be well prepared for the final exam:
- Make sure you understand the concepts from the homeworks
- Understand the minimax algorithm.
- How to prove that a problem is NP complete.
- Floyd's algorithm / shortest paths
- Principle of Optimality
- Deriving the big-Theta bounds for certain kinds of divide-and-conquer algorithms using the methods for solving recurrence relations covered in class. In particular, be sure to be able to do the first problem in the last homework assignment on recurrences. This will involve knowing some logarithm and exponent identities.
- How to turn a problem description into a linear programming problem using standard form, slack form, or simplex form (know all three ways).
- Understand the various knapsack problems
- Understand how depth-first and breadth-first search work.
- Design decisions in creating and implementing branch and bound algorithms.
- Basics of solving the TSP with a branch and bound algorithm.
- How to compute amplification of stochastic advantage for repeated Monte Carlo algorithms.
- How to compute speedup and efficiency for parallel algorithms and how to tell if a parallel algorithm is good or not.
If you study these topics, and understand them, you should be ready for the final.