A practical introduction to the main topics in algorithms, databases, and tools used in bioinformatics. Includes public databases such as Genbank and PDG, software tools such as BLAST, and their underlying algorithms. Use of Perl scripting language to perform a number of useful tasks in analyzing sequence data and managing bioinformatic databases.

Fundamental techniques for designing efficient computer algorithms, proving their correctness, and analyzing their complexity. General topics include sorting, selection, graph algorithms, and basic algorithm design paradigms (such as divide-and-conquer, dynamic programming and greedy algorithms), lower bounds and NP-completeness.

An introduction to representations and algorithms that allow machines to improve their performance at tasks from experience. Topics include: linear classification, decision tree neural networks, support vector machines, relational learning, reinforcement learning, and clustering algorithms such as k-means and hierarchical agglomerative clustering.

The writing of technical papers and reports.

Curves in the plane and Euclidean space, moving frames, surfaces in Euclidean space, orientability of surfaces; Gaussian and mean curvatures; surfaces of revolution, ruled surfaces, minimal surfaces, special curves on surfaces, "Theorema Egregium"; the intrinsic geometry of surfaces.

An abstract treatment of finite dimensional vector spaces. Linear transformations and their invariants.