ITAI 1371 Introduction to Machine Learning

Introduction to machine learning concepts and Python applications, including data acquisition, supervised and unsupervised learning, and data modeling.

Credits

3

Offered

Spring

Outcomes

1. Describe Machine Learning and how Deep Learning is a subset of the wider field of Machine Learning.
2. Interpret and understand the basic tools required for building Machine Learning Projects through a selected list of topics from Mathematics and Python Programming.
3. Develop and create a simple dashboard for visualizing data through Data Visualization tools such as Tableau.
4. Classify and compare different models available in Supervised Learning, Unsupervised Learning, and Reinforcement Learning.
5. Describe common terms and concepts used in the different steps of the AI Project Cycle like Accuracy, Precision, Recall, F1 Score, Underfitting, Overfitting, etc.
6. Describe common terms and concepts used in the different steps of the AI Project Cycle like Accuracy, Precision, Recall, F1 Score, Underfitting, Overfitting, etc.
7. Develop Python-based use cases and AI Projects incorporating each of the following methods - Supervised Learning, Unsupervised Learning and Deep Neural Networks.
8. Discuss and interpret the future of ML based on current and upcoming trends.