International Machine Learning Competition (IMLC)

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Machine Learning Presentations

We are pleased to share an extensive collection of machine learning presentations. These slides are sourced from external institutions and cover a broad range of topics.

(To report broken links, please send an e-mail to: info@imlco.org)

📄 Introduction to Machine Learning (Download)
Author: Marzyeh Ghassemi
Institute: MIT

📄 Introduction to Machine Learning and Nearest Neighbors (Download)
Author: Roger Grosse
Institute: University of Toronto

📄 Unsupervised Learning and Clustering (Download)
Author: Dennis Sun
Institute: Stanford University

📄 Linear and Logistic Regression (Download)
Author: Sherrie Wang
Institute: Stanford University

📄 Decision Trees and Random Forests (Download)
Author: Sherrie Wang
Institute: Stanford University

📄 Support Vector Machines (Download)
Author: Sherrie Wang
Institute: Stanford University

📄 Clustering and K-Means (Download)
Author: Enfa George
Institute: University of Arizona

📄 Introduction to Neural Networks and Deep Learning (Download)
Author: Efstratios Gavves
Institute: University of Amsterdam

📄 Backpropagation and Neural Networks (Download)
Author: Fei-Fei Li, Justin Johnson, Serena Yeung
Institute: Stanford University

📄 Convolutional Neural Networks (Download)
Author: Fei-Fei Li, Justin Johnson, Serena Yeung
Institute: Stanford University

📄 Introduction to Recurrent Neural Networks (Download)
Author: Sebastian Raschka
Institute: University of Wisconsin-Madison

📄 Gradient Descent and Optimization (Download)
Author: Stanley Chan
Institute: Purdue University

📄 Overfitting and the Bias-Variance Trade-off (Download)
Author:
Institute: Carnegie Mellon University

📄 Model Evaluation, Assessment and Selection (Download)
Author: Joelle Pineau
Institute: McGill University

📄 Introduction to NLP and Word Vectors (Download)
Author: Christopher Manning
Institute: Stanford University

📄 Introduction to Reinforcement Learning (Download)
Author: David Silver
Institute: Stanford (CME 241)

📄 Introduction to Transformers and Attention (Download)
Author: Dan Jurafsky
Institute: Stanford University