introml
Table of Contents
The course is taught in a sequence of units. Each unit takes between one and two weeks so that the entire class can be fit into a single semester. Most units currently have four or five components:
- Lecture Notes: These are slides accompanying the class lecture. They include code snippets from the demos.
- Lecture Videos: These are videos of the lectures. Right now, the videos for only a few units have been recorded, but I am hoping to add more.
- Demo: These are python-based Jupyter notebooks for demonstrations given during the lectures. Some demos have a component that is done in class. The demos do not generally cover all topics, since some concepts are left for the students to figure out for themselves in the labs.
- Lab: Following the lecture, the students do a python-based exercise at home
that builds on the demo.
The labs in the repository are given as skeletons with
TODOmarkers that the students fill in. - Problems: These are more analytic problems, also done at home.
The problem and lab solutions are provided to students enrolled in the class. If you are an instructor and wish copies of the solutions for yourself, please contact Sundeep Rangan at srangan@nyu.edu.
This site is undergoing re-numbering of units, so many links may connect to documents that don’t match the number of the unit. We will fix these soon!
- Setting up python, jupyter and github
- Introduction
- Course Admin [pdf] [Powerpoint]
- Unit 1: What is machine learning?
- Lecture: Introduction to Machine Learning [pdf] [Powerpoint]
- Lecture videos
- Demo: Introduction to numpy vectors
- Unit 2: Simple linear regression
- Lecture: Simple linear regression [pdf] [Powerpoint]
- Demo: Understanding automobile mpg
- Lab: Boston housing data
- Problems [pdf] [Latex]
- Unit 3: Multiple linear regression
- Lecture: Multiple linear regression [pdf] [Powerpoint]
- Lecture videos
- Demo 1: Predicting glucose levels
- Demo 2: Python broadcasting
- In-class Exercise
- Lab: Calibrating robot dynamics
- Problems [pdf] [Latex]
- Unit 4: Model selection
- Unit 5: Regularization and LASSO
- Lecture: LASSO Regularization [pdf] [Powerpoint]
- Lecture videos
- Demo 1: Predicting prostate cancer
- Demo 2: Predicting housing prices
- Lab: EEG source localization
- Problems [pdf] [Latex]
- Unit 6: Logistic regression
- Lecture: Linear classification and logistic regression [pdf] [Powerpoint]
- Lecture videos
- Demo: Breast cancer diagnosis via logistic regression
- Lab: Genetic analysis of Down’s syndrome in mice
- Problems [pdf] [Latex]
- Unit 7: Nonlinear optimization
- Lecture: Nonlinear optimization and gradient descent [pdf] [Powerpoint]
- Lecture videos
- Demo 1: Computing gradients
- Demo 2: Simple gradient descent optimization
- Lab: Nonlinear least squares material modeling
- Problems [pdf] [Latex]
- Unit 8: Support vector machines
- Unit 9: Neural networks with Keras and Tensorflow
- Lecture: Neural networks [pdf] [Powerpoint]
- Lecture videos
- Supplementary notes with solved problems [pdf] [Latex]
- Demo 1: First neural network in Keras
- Demo 2: MNIST neural network classification
- Lab: Music instrument classification
- In-class Exercise
- Problems: [pdf] [Latex]
- Unit 10: Convolutional and deep networks
- Lecture: Convolutional and deep networks [pdf] [Powerpoint]
- Setting up a GPU instance (Recommended)
- Lecture videos
- Demo 1: 2D convolutions and convolutional layers in keras
- Demo 2: Creating an image set using the Flickr API
- Demo 3: Exploring the deep VGG16 network
- Demo 4: Building an image classifier using CIFAR10 dataset
- Demo 5: Building an autoencoder for image denoising using CIFAR10 dataset
- Lab: Transfer learning with a pre-trained network (GPU recommended)
- Problems [pdf] [Latex]
- Unit 11: PCA
- Lecture: PCA [pdf], Modifed Note [pdf] [Powerpoint]
- Lecture videos
- Demo 1: PCA eigen-faces-SVM
- Demo 2: Low-rank matrix completion via embedding layers
- Lab: PCA with hyper-parameter optimization
- Problems [pdf] [Latex]
- Unit 12: Introduction to Text
- Lecture: Introduction to Text [pdf] [Powerpoint]
- Demo 1: Tokenization
- Demo 2: Movie reviews
- Unit 13: Clustering and EM
- Lecture: Clustering and EM [pdf] [Powerpoint]
- Demo 1: Document clustering via k-means and latent semantic analysis
- Demo 2: Color quantization via k-means and EM-GMM
- Homework [pdf] [Latex]
- Unit 13: Decision Trees and Random Forest
- Lecture: Decision tree and random foreast [pdf] [Powerpoint]
- Demo: Prediction of temperature using decision tree and random forest
- Homework [pdf] [Latex]