CS480/680 Lecture 1: Course Introduction

(1:4:49)

CS480/680 Lecture 19: Attention and Transformer Networks

(1:22:38)

CS480/680 Lecture 6: Fact checking and reinforcement learning (Vik Goel)

(8:31)

CS480/680 Lecture 5: Statistical Linear Regression

(1:13:25)

CS480/680 Lecture 16: Convolutional neural networks

(1:15:18)

CS480/680 Lecture 20: Autoencoders

(38:58)

CS480/680 Lecture 11: Kernel Methods

(1:16:14)

CS480/680 Lecture 23: Normalizing flows (Priyank Jaini)

(1:5:23)

CS480/680 Lecture 7: Mixture of Gaussians

(1:59)

CS480/680 Lecture 13: Support vector machines

(1:17:43)

CS 480/680 - F24 - L20 - Robustness - Guest Lecture from Dr. P. Michael Furlong

(1:20:20)

CS 480/680 - F24 - L1 - Halfspaces \u0026 The Perceptron Algorithm

(54:9)

CS480/680 Lecture 21: Generative networks (variational autoencoders and GANs)

(55:51)

CS 480/680 - F24 - L4 - Non-Parametric Methods: KDE, k-NN, and k-Means

(54:50)

17. Learning: Boosting

(51:40)

CS480/680 Lecture 8: Logistic regression and generalized linear models

(1:34:44)

CS480/680 Lecture 6: Kaggle datasets and competitions

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CS480/680 Lecture 15: Deep neural networks

(1:31:6)

CS480/680 Lecture 12: Gaussian Processes

(1:11:15)

CS480/680 Lecture 6: Model compression for NLP (Ashutosh Adhikari)

(6:24)

CS480/680 Lecture 18: Recurrent and recursive neural networks

(1:40:41)

CS480/680 Lecture 22: Ensemble learning (bagging and boosting)

(1:7:24)

CS480/680 Lecture 6: Unsupervised word translation (Kira Selby)

(9:13)

CS480/680 Lecture 6: Tools for surveys (Paulo Pacheco)

(9:59)

CS480/680 Lecture 14: Support vector machines (continued)

(28:42)

CS480/680 Lecture 9: Perceptrons and single layer neural nets

(1:16:23)

CS480/680 Lecture 3: Linear Regression

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