Statistics and Computing Course ID 15260 Description Statistics is essential for a wide range of fields including machine learning, artificial intelligence, bioinformatics, and finance. This mini course presents the fundamental concepts and methods in statistics in six lectures. The course covers key topics in statistical estimation, inference, and prediction. This course is only open to students enrolled in 15-259. Enrollment for 15-260, mini 4, starts around mid semester. Key Topics Point estimation, Bayesian estimation, hypothesis testing, confidence intervals, classification, regression Required Background Knowledge This Statistics module is only available to students concurrently enrolled in 15-259. No other prerequisites. Course Goals Design estimators for parameters of distributions using likelihood functions or Bayesian methods. Design and analyze tests for hypothesis testing. Formulate classification problems and construct classifiers. Understand regression and its applications. Assessment Structure Homework -- worth 80%. In class quizzes -- worth 20%
