Compt. and Algorith. Thnking

1 Credit · Level: Graduate

This course introduces fundamental algorithms used in bioinformatics, emphasizing both conceptual understanding and practical problem-solving. Students will study key computational methods including sequence alignment algorithms (e.g., Needleman-Wunsch, Smith-Waterman), string matching algorithms (e.g., Boyer-Moore, Knuth-Morris-Pratt), clustering techniques (k-means, DBSCAN), and tree-based data structures such as binary search trees. In addition to mastering these specific algorithms, students will learn to generalize core principles and apply them to novel or related computational problems. The course emphasizes step-by-step exercises completed on paper to build algorithmic thinking, reinforce theoretical knowledge, and prepare students to adapt solutions beyond standard cases.