02-680 Essential Mathematics and Statistics for Scientists
02-680 COURSE PROFILE
|Special Permission Required? (If yes, see “Notes:)||No|
|Course Relevance (who should take this course?)||This course rigorously introduces fundamental topics in mathematics and statistics to first-year master’s students. It directly prepares students for 02-620 (Machine Learning for Scientists) and gives students the quantitative foundation needed for advanced courses that apply concepts in machine learning to scientific datasets, such as 02-710 (Computational Genomics) and 02-750 (Automation of Biological Research).|
|Key Topics||Topics are sampled from information theory, graph theory, proof techniques, phylogenetics, combinatorics, set theory, linear algebra, neural networks, probability distributions and densities, multivariate probability distributions, maximum likelihood estimation, statistical inference, hypothesis testing, Bayesian inference, and stochastic processes.|
|Background Knowledge||There are no formal prerequisites. However, we expect that students will have a strong foundation in high school mathematics (including calculus) and possess strong quantitative reasoning skills, as the course will be taught at a high level and proceed quickly.|
Homework assignments (40% of grade) Written homework assignments will test your knowledge of the material covered in class.
Attendance and participation (10% of grade) Attendance will be taken, and we will have occasional in-class exercises that serve to reinforce the concepts we have covered. These exercises will not be graded, but participation will be expected in order to receive a complete grade for that day. You are allowed three “dropped” attendance grades without penalty. These can be used for any purpose.
Examinations (50% of grade) Two midterms test knowledge of the material from the class.
|Most Recent Syllabus||02-680 Syllabus Fall 2019|
|Course Goals/Objectives||Students completing this course will obtain a broad skillset of mathematical techniques and statistical inference as well as a deep understanding of mathematical proof. They will have the quantitative foundation to immediately step into an introductory master’s level machine learning or automation course.|
|Pre-reqs, Cross List, Related||None|
|Updated February 2020|