Notes

• Assessing ML workflow performance
• ML best practices
• Assembling the transformer
• Attention and the transformer
• Physics-informed neural networks
• Neural networks: worked examples
• Neural networks: continued
• Neural networks: more architectures
• Neural networks (beginning with the perceptron)
• Logistic regression
• Gradient descent
• Classification, continued
• Classification
• Clustering
• Fundamental concepts in AI/ML
• Dimensional Reduction
• Filtering and resampling
• Data characterization
• Accessing data
• Data: Types, formats, and availability
• Introductions
• Putting together your coding ecosystem
• An introduction to ESS 469/569