These are notebooks developed for ECE-GY 6143 Intro to Machine Learning at NYU Tandon School of Engineering.
- Notebook: Python + numpy tutorial
- Notebook: Colab tutorial
- Notebook: Printing from Colab
- Notebook: Exploratory data analysis (in-class)
- Notebook: Exploratory data analysis (homework)
- Notebook: Data detective challenge (optional homework)
- Notebook: Linear regression in depth
- Notebook: Compute regression coefficients by hand
- Notebook: Regression metrics
- Notebook: Case study on "Beauty in the Classroom"
- Notebook: Residual analysis on Advertising data (homework)
- Notebook: Bias-variance tradeoff and model selection in depth
- Notebook: Model order selection for neural data (homework)
- Notebook: Logistic regression in depth
- Notebook: Logistic regression for handwritten digits classification
- Notebook: COMPAS case study
- Notebook: Classifying your own handwritten digit (homework)
- Notebook: K nearest neighbor in depth
- Notebook: Voter classification with K nearest neighbor (homework)
- Notebook: Decision trees and ensembles
- Notebook: AdaBoost
- Notebook: Bias and variance of KNN and decision tree models
- Notebook: Support vector machines
- Notebook: Handwritten digits classification
- Notebook: Bias and variance of SVM
- Homework: Grid search for SVM hyperparameter tuning (use NYU Google account to open)
- Notebook: Backpropagation from scratch
- Notebook: Draw your own classification problem for a neural network
- Homework: Neural network for musical instrument classification (homework) [
- Deep dive: Convolutional neural networks (use NYU Google account to open)
- Transfer learning (use NYU Google account to open)
- Homework: Transfer learning
For your project, you will replicate and then extend an existing ML project (typically a recent publication in a major ML conference). See projects list for examples.