# Stat 215a: Applied Statistics and Machine Learning

UC Berkeley

## Offerings

## Overview

Applied statistics and machine learning, focusing on answering scientific questions using data, the data science life cycle, critical thinking, reasoning, methodology, and trustworthy and reproducible computational practice. Hands-on-experience in open-ended data labs, using programming languages such as R and Python. Emphasis on understanding and examining the assumptions behind standard statistical models and methods and the match between the assumptions and the scientific question. Exploratory data analysis. Model formulation, fitting, model testing and validation, interpretation, and communication of results. Methods, including linear regression and generalizations, decision trees, random forests, simulation, and randomization methods.

## Logistics

Three hours of lecture and two hours of laboratory per week.

## Prerequisites

Linear algebra, calculus, upper division probability and statistics, and familiarity with high-level programming languages. Statistics 133, 134, and 135 recommended.