Applied Machine Learning (Cornell CS5785, Fall 2024)

This repo contains executable course notes and slides for the Applied ML course at Cornell and Cornell Tech (Fall 2024 edition).

Note that these notes are slightly different from the ones used in my Youtube lecture videos videos from the Fall 2020 edition of the course. You may find these in my other Github repo.

Contents

This repo is organized as follows.

.
├── README.md
├── slides                # Course slides
├── lecture-notes         # Lecture notes (expanding on the material in the slides)
└── requirements.txt      # Packages needed for your virtualenv

Tentative Schedule

PDFs of the slides — please check back before the lectures as we will be updating the slides and PDFs throughout the semester.

Date	Lecture
8/26/2024	Introduction: Supervised, unsupervised, reinforcement learning
8/28/2024	In-Class Tutorial: Linear Algebra, Probability
9/4/2024	[SL] Introduction. Models, features, objectives, optimization
9/9/2024	[SL] Regression. Linear Regression. OLS
9/11/2024	[SL] Classification. Logistic Regression and Max Likelihood
9/16/2024	[SL] Why Does SL Work? Data distribution, over/under fitting, regularization
9/18/2024	[SL] Generative Classifiers and Gaussian Discriminant Analysis
9/23/2024	[SL] Naive Bayes, bag of words
9/25/2024	Guest Lecture by Guan-Horng Liu
9/30/2024	[UL] Introduction to Unsupervised Learning. K-Means
10/2/2024	[UL] Clustering. Gaussian mixture models, expectation-maximization
10/7/2024	[UL] Dimensionality Reduction. PCA
10/9/2024	[UL] Visualization and embeddings. MDS and TSNE
10/14/2024	Fall Break - No class
10/16/2024	[SL] SVMs. Margins, max-margin classifiers, hinge loss, optimization
10/21/2024	[SL] Dual Formulation of SVMs. Lagrange duality, SVMs duals, SMO
10/23/2024	[SL] Kernels. Kernel trick, examples of kernels, Mercer's theorem
10/28/2024	Prelim Review
10/30/2024	Prelim In Class
11/4/2024	[SL] Neural Networks. Perceptrons, multi-layer neural networks
11/6/2024	[SL] Deep Learning. Convolutional neural networks and applications
11/11/2024	[SL] Advanced Deep Learning. ResNets, RNNs
11/13/2024	[SL] Advanced Deep Learning. Transformers/LLMs
11/18/2024	[SL] Decision Trees. Bagging, ensembling, CART
11/20/2024	[SL] Boosting. Adaboost, gradient boosting
11/25/2024	Guest Lecture
11/27/2024	Thanksgiving Break - No class
12/2/2024	Machine Learning: Diagnosis. Model iteration process, bias/variance tradeoff, baselines, learning curves
12/4/2024	Applying Machine Learning: Diagnosis. Error analysis, data integrity, human-level performance
12/9/2024	Final Lecture. Overview of the course. Taxonomy of ML algorithms. Research directions

Setup

Requirements

You should be able to run all the contents of this repo using the packages provided in requirements.txt.

In a new virtualenv, run this:

pip install -r requirements.txt

Feedback

Please send feedback to Volodymyr Kuleshov