Course Requirements and Grading: The course consists of three classes every week (lectures on Tuesday and Thursday morning and lab on Friday morning). Attendance to all lectures and labs is required, unless otherwise pre-approved.

Grading is based on class participation (5%), reading responses (20%), labs (25%), midterm exam (20%), and final project (30%).

Attendance is mandatory and you will be expected to discuss concepts covered in class with your peers. Each lecture will be divided into modules and peer discussion will be an integral part of each module. In order for these discussions to be fruitful, doing the readings prior to class is necessary. The readings may be dense, but don’t let that intimidate you! Do your best, and challenging concepts will be clarified in class.

Reading responses should be completed individually, contain summaries of the assigned reading no more than one page in length, and touch on all major topics of the readings. The preferred form of these notes is latex-based pdf, but any electronic format is acceptable (word, powerpoint, text file, etc.). Reading responses should be uploaded to courseworks prior to the associated lecture. Late reading responses will be given partial credit. Reading responses should be unique and authored by you alone while reading the assignments. Do not use the notes of others or other materials for this assignment – this will be checked.

Each lab write-up must be submitted through courseworks by the Monday following the lab.

Final projects can be completed in groups of no more than two students. The final project should integrate concepts from at least 3 separate lectures in a meaningful way. A project proposal describing the problem being addressed (could be a clinical and/or computational problem), the dataset being analyzed (could be simulated data), methods, baseline methods, the team, and each member’s contribution to the project. The final project proposal should be no more than one page in length and is due one week after the midterm exam.

Although you are free to choose any health-related dataset, example datasets that could be used include:
● Physionet (Requires short ethics courses and approval. Speak to me before requesting
access)
  ○ Physiobank
  ○ MIMIC
  ○ EICU
● The Cancer Genome Atlas
● Simons Genome Diversity Project
● 1000 Genomes
● NIH X-ray Database
● Dream Challenges
● Grand Challenges
● Health-related challenges on Kaggle
● dbGaP (request for data must go through me and requires several months advance notice)

Final project presentations will be scheduled near the end of the semester and will be 5-10 minutes long depending on how many groups are presenting. There is no length requirement for the final write-up, but it should discuss the following topics (typically ~5-10 pages):
● What is the clinical/computational problem?
● How is it addressed today, if at all?
● What is limiting about the current approaches?
● What is better about your approach?
● How will we know that you are successful?
● Methods, including one or more baseline methods (baseline methods may be simple)
● Data description
● Results
● Discussion/Conclusions