Artificial Intelligence

In Introduction to Artificial Intelligence and Machine Learning, students will learn the basic concepts, algorithms and libraries used in AI and machine learning. 

Foundation

• Introduction to AI and AI applicationsWhat is Artificial Intelligence?

  • Creating Models with online Tools

  • Intro to Supervised ML

• Python - Dictionaries, NumPy, Matplotlib

  • Review of strings, lists, functions

  • Dictionaries, reading from files

  • NumPy and Matplotlib

• Algorithms: Clustering, Decision Trees

• Data and Bias

Machine Learning

• Linear RegressionCost Function

• Gradient Descent

• Introduction to Feature Engineering: Normalization

Deeper Dive into ML

• Logistic Regression

• Feature Engineering: Categorical Data

• Feature Engineering: Missing Data

Neural Networks

• Perceptron

• Neural Networks

• Sentiment Analysis with a NN

• Backpropagation

Special Topics

Natural Language Processing

Image Recognition

Final Project

Embedded throughout the Semester we will investigate the ethics and impact of AI. Students will be required to address potential concerns and potential impacts in their projects.