Students will be introduced to machine learning and artificial intelligence through interactive activities and programming. The first two lessons will be focused on the principles of machine learning and how it's different from traditional programming. By collecting data and training models themselves, students will get an inside look into how machine learning works. In the third and last lessons, students will train simple machine learning models in Python and recognize how algorithmic bias can arise. 

This camp is designed for students who are curious about AI and machine learning and who have had some exposure to programming (i.e. Scratch, Java, or Python). No need to be intimidated though - we will start from scratch in this camp! 

Learning objectives (what students will be able to do after each lesson): 

Lesson 1: 
- Understand how machine learning is different from traditional programming
- Know machine learning uses a variety of algorithms that iteratively learn from data to improve and predict outcomes
- Define "big data"
- Recognize ML applications in everyday life

Lesson 2: 
- Understand the differences between different approaches to machine learning (supervised vs. unsupervised)
- Describe the input and output of a classification model
- Explain how AI classifies data by recognizing patterns 

Lesson 3: 
- Understand how machine learning is used to solve real problems
- Write snippets in Python to recognize different fruits
- Implement ML techniques using Python libraries

Lesson 4: 
- Learn about instances of biased machine learning algorithms in the real world
- Complete a loan approval program in Python
- Understand how bias can arise through data even unintentionally
- Proactively think about the consequences of algorithms

*Students must have a Google account*
We keep class sizes small to ensure every student is fully engaged. This class has a minimum enrollment of 2 learners.