![]() Therefore you need to regularly log onto Compass course site to keep up with the course. All contents will be accessible on the Compass course site including lecture notes, lecture videos, discussion videos, quizzes, HW, team assignments and project, course helps, etc. Office hours will be offered through Zoom as well. The lectures and discussions will be offered through live Zoom meetings. This is a 3 credit hour course that lasts 16 weeks. We have built a brand new Compass 2G course site that includes all the materials (text or video), information on course help, resources and links to various course tools while still maintaining the course public website in the same format as previous semesters.We will make changes regarding office hours if students are in a timezone that makes the OH difficult for them.We have also increased the office hours from the instructor, the TAs and the CAs.We have added promotional extra points for students to involve more in office hours, interaction with the instructor and community building.We have added extra points for group exercises in discussion sessions.We have added optional team review/work component for students to keep in close contact with peers in the class and have more experience of learning within the community.To help study along the course in an online format, we have rebalanced the grade points to have a Compass online quiz part for you to check where you are and get feedbacks immediately.In addition, video recordings of lectures and discussions will be made available shortly after each meeting. ![]() All lectures and discussions will be offered via synchronous (real-time) Zoom meetings.We have made a variety of adjustments to help you have a great experience this Fall remotely. Markov inequality, Chebyshev inequality, law of large numbers, simulation, populations and sampling, sample mean, standard error, maximum likelihood estimation, Bayes estimation, hypothesis testing, confidence intervals, linear regression, principal component analysis, classification, clustering methods, Markov chains and the PageRank algorithm.Ĭourse adjustments in response to the COVID-19 pandemicĪs a large class, we think it is not possible for us to safely deliver course contents on-campus physically in-person this semester. Topics include: visualizing datasets, summarizing data, basic descriptive statistics, conditional probability, independence, Bayes theorem, random variables, joint and conditional distributions, expectation, variance and covariance, central limit theorem. ![]() It is foundational for more advanced computer science courses including data science and machine learning. This course gives an introduction to probability theory and statistics with applications to computer science. ![]()
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