Read leisurely...
Content to emphasize: big-Oh notation, and priority queue data structure (heaps). Beyond actual textbook material, we did some basics of Boolean and propositional logic such as quantifiers (which are important for manipulating big-Oh notation).
Content to know: BFS, DFS, DAGs, Topological sorting.
Read sections 1 to 8 (skip 9). Some key concepts: interval scheduling, shortest paths, MST, Huffman code.
Read sections 1 to 5 (Skip 6). Problems to know: mergesort (counting inversions), closest pair, Karatsuba multiplication.
The following are covered in lectures and goes slightly beyond the book: know how to solve recurrences using (1) Rote method and (2) Real induction method. We also want you to know the Master Theorem for the case T(n)=aT(b/n)+f(n) where f(n) is a polynomial.
Read sections 1 to 9 (skip 10). This is a big chapter. Here are the key problems to know: (1) Weighted Interval Scheduling, (2) Subset Sum and Knapsack, (3) RNA secondary structure, (4) DNA alignment problem, (5) Shortest path with negative weights. Key words: dynamic programming principle, number of subproblems, memoization, computing optimum value versus computing optimum solution.
Skip this completely.
Read sections 1 to 4 only. You need to understand the concept of reducibility and NP-completeness. Should know how to do basic reductions among problems. Also, material we covered in lectures.
Read sections 1 to 5. Most of this is informal, but we would like you to understand the concepts of local search as applied to Vertex Cover, and to know the Hopfield neural network problem as described in the text.