Pre-College Summer Programs and Courses
Accelerate your entry into college life in a fast-paced environment while earning academic credit from a top university.
Filter your search by area of study, location, and date range. Qualified pre-college students may also consider the undergraduate courses listed below.
Foundational Mathematics of Artificial Intelligence - AS.110.110
Pre-College students June 22 - July 1 Homewood CampusAs artificial intelligence models like ChatGPT and Claude become increasingly sophisticated, understanding how they work is more important than ever. This course introduces students to the mathematical and statistical principles behind machine learning and AI technologies. Students will learn the mathematical concepts behind classification and prediction models and implement these models in Python. Working with real-world data, students will design machine learning applications that power modern AI systems. Models studied include linear regression, classification trees, neural networks, and K-nearest neighbors (KNN). By testing and improving their models, students will gain insight into both the possibilities and limitations of AI.
Students in this course must bring a laptop capable of opening a spreadsheet, running cloud-based code, and running cloud-based programs like Microsoft Word and PowerPoint.
Prerequsite: Algebra I.
Foundational Mathematics of Artificial Intelligence - AS.110.110
Pre-College students July 6 - July 16 Homewood CampusAs artificial intelligence models like ChatGPT and Claude become increasingly sophisticated, understanding how they work is more important than ever. This course introduces students to the mathematical and statistical principles behind machine learning and AI technologies. Students will learn the mathematical concepts behind classification and prediction models and implement these models in Python. Working with real-world data, students will design machine learning applications that power modern AI systems. Models studied include linear regression, classification trees, neural networks, and K-nearest neighbors (KNN). By testing and improving their models, students will gain insight into both the possibilities and limitations of AI.
Students in this course must bring a laptop capable of opening a spreadsheet, running cloud-based code, and running cloud-based programs like Microsoft Word and PowerPoint.
Prerequsite: Algebra I.
Foundational Mathematics of Artificial Intelligence - AS.110.110
Pre-College students July 20 - July 30 Homewood CampusAs artificial intelligence models like ChatGPT and Claude become increasingly sophisticated, understanding how they work is more important than ever. This course introduces students to the mathematical and statistical principles behind machine learning and AI technologies. Students will learn the mathematical concepts behind classification and prediction models and implement these models in Python. Working with real-world data, students will design machine learning applications that power modern AI systems. Models studied include linear regression, classification trees, neural networks, and K-nearest neighbors (KNN). By testing and improving their models, students will gain insight into both the possibilities and limitations of AI.
Students in this course must bring a laptop capable of opening a spreadsheet, running cloud-based code, and running cloud-based programs like Microsoft Word and PowerPoint.
Prerequsite: Algebra I.