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There are two goals for this textbook. The first goal is to introduce you to the foundations of the field of computational biology. Namely, introduce the fundamental biological problems of the field, and learn the algorithmic and machine learning techniques needed for tackling them. This goes beyond just learning how to use the programs and online tools that are popular any given year. Instead, the aim is for you to understand the underlying principles of the most successful techniques that are currently in use, and provide you with the capacity to design and implement the next generation of tools. That is the reason why an introductory algorithms class is set as a pre-req; the best way to gain a deeper understanding for the algorithms presentedis to implement them yourself.

The second goal of the textbook is to tackle the research frontiers of computational biology, and that’s what all the advanced topics and practical assignments are really about. We’d actually like to give you a glimpse of how research works, expose you to current research directions, guide you to find the problems most interesting to you, and help you become an active practitioner in the field. This is achieved through guest lectures, problem sets, labs, and most importantly a term-long independent research project, where you carry out your independent research.

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