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DSC 340: Machine Learning and Neural Networks
Lecture Notes
Introduction to Machine Learning and the Mathematics of Machine Learning
Linear Regression and 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
Slides
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
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