# DSC 340: Machine Learning and Neural Networks#

This website contains lecture notes and slides for DSC 340: Machine Learning and Neural Network Processing, taught at the University of Mount Union in Fall 2023. The table of contents is located on the left side bar, with each link represent the lecture notes or slides for the entire week.

Any questions or comments should be directed to Dr. Julie Butler at juliebutler@juliebutler.org

- Introduction to Machine Learning and the Mathematics of Machine Learning
- Linear Regression and the Machine Learning Workflow
- The Machine Learning Workflow
- Regularized Linear Models
- Using Machine Learning to Solve Classification Problems
- Model Optimization and Nonlinear Models
- Unsupervised Machine Learning: Clustering and Dimensionality Reduction
- A Conceptual and Mathematical Introduction to Neural Networks
- Creating Neural Networks with Scikit-Learn and Keras
- Creating Neural Networks with Tensorflow
- Creating Neural Networks from Scratch

- Introduction to Machine Learning
- Linear Regression and the Machine Learning Workflow
- Classification Problems
- Model Optimization and Nonlinear Models
- Unsupervised Machine Learning: Clustering and Dimensionality Reduction
- A Conceptual and Mathematical Introduction to Neural Networks
- Creating Neural Networks with Scikit-Learn and Keras
- Creating Neural Networks with Tensorflow
- Creating Neural Networks from Scratch
- Introduction to Convolutional Neural Networks
- Advanced Methods for Improving the Performance of Convolutional Neural Networks
- Recurrent Neural Networks