In this course, we will study a variety of different techniques for design and analyzing algorithms. We will mostly focus on problems for which the exact algorithm is not known or not efficient enough and problems with resource constraints. Besides designing efficient algorithms, proving the performance guarantees is also a major topic of this course. Some topics that we will cover are as follows:

1. Approximation Algorithms: Algorithms that find near-optimal solutions with provable performance guarantees in polynomial time. In this course, we will mostly focus on approximate algorithms for NP-hard problems.

2. Randomized Algorithms: Algorithms that use random numbers. We will focus on algorithms with provable success probabilities and/or good expected solution quality.

3. Streaming Algorithms: Algorithms that solves problems on huge datasets. In this type of problem, usually the algorithm is only allowed to read the data once and use no more than a constant or poly-logarithmic amount of space.

4. Online Algorithm: The input to the problem is not known in advance and arrives over time. An online algorithm needs to make decision on how to process a specific input before seeing future inputs. The goal is to perform as well as an algorithm that knows all inputs beforehand.

We will cover different algorithm design techniques such as hashing, sampling and linear programming.