Program Overview

Program Details

The provided content does not contain a comprehensive university program but rather a course description. However, based on the given instructions, the extracted information related to the course can be presented as follows:

Course Description

This course is designed to expose students to the complex real-world datasets commonly used in machine learning applications. The course provides an accessible introduction to supervised machine learning, while covering aspects of data collection and cleaning. Specific topics include model construction, evaluation, and regularization, as well as web scraping, text data, feature construction, and measurement error. Students complete short assignments, longer homework sets, and a final project.

Course Information

• Course #: APSTA-GE 2047
• Credits: 3
• Department: Applied Statistics, Social Science, and Humanities
• School: Steinhardt School of Culture, Education, and Human Development

Given the constraints and the nature of the input, the above information is the most relevant and detailed content that can be extracted and presented in the required format. The input does not contain a broader university program description but focuses on a specific course within a program. Therefore, the extracted details are limited to this course. If the input were to include more comprehensive program information, the extraction would naturally encompass more details such as admission criteria, tuition fees, and research areas, among others. However, based on the provided content, the above represents the most accurate and detailed extraction possible.