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Week 1: Background Review (Math)

• Princeton NEU 314 Sections 1 through 2.1 (Least squares with vector calculus)
• Stanford cheat sheets on ML and math
• Calculus for ML cheat sheet
• Linear Algebra Review

Week 1: Intro to ML

• Pandas API reference
• Numpy API reference
• Data 100 (specifically the course notes)
• CS197 Harvard: Moonwalking with PyTorch (neural networks!)

Week 2: Classical ML

• Bias and Variance
• Gradient Descent
• A Guided Tour Through Classical Machine Learning Algorithms

Week 3: Deep Learning

• 3Blue1Brown Course
• Deep Dive on Back Prop Video
• Optimizers
• Micrograd - Try to Build Backprop Yourself (by Andrej Karpathy!)

Week 4: Convoltions & CNNs

• Understanding Convolutions (old NMEP HW!)
• CNNs Visualized
• Simple CNN Explanation
• MIT Convolutional Neural Networks
• Convolutional Neural Networks (LeNet)

Week 5 & 6: Transformers

• Data C182 (part 2 and part 4 of the lecture on transformers)
• The Illustrated Transformer
• Constituency Parsing
• Machine Translation Evaluation Metrics
• Show, Attend, and Tell: Neural Image Caption Generation (all about visual transformers)
• The Annotated Transformer (PyTorch code walkthrough!)

Week 8: Object Detection

• Understanding of Object Detection
• A comprehensive review of object detection
• Deep learning models for classification

Week 10: GANs

• Foundational Paper
• Unsupervised Representational Learning with GANs
• Generating Basketball Shoes

Papers

• How to read a paper
• Visualizing loss landscape of neural nets