Lecture Videos

In this video, we introduce basic concepts on how to regularize regression using L2 and L1 norms. Watch on:FAU TVYouTube

This video discusses normalization such as batch normalization and self normalizing units and explains the concepts of drop-out and drop-connect Watch on:FAU TVFAU TV (no memes)YouTube Read the Transcript (Summer 2020) at:LMETowards Data Science

In this video, we look into some example applications of LDA and PCA. Watch on:FAU TVYouTube

This video discusses classical regularization techniques such as early stopping using a validation set, augmentation, and maximum a-posteriori methods that expand the loss function using a regularization term. Watch on:FAU TVFAU TV (no memes)YouTube Read the Transcript (Summer 2020) at:LMETow...

In this video, we have a look at the original formulation of LDA as Fisher Transform. Watch on:FAU TVYouTube

This video discusses the problem of over- and underfitting. In order to get a better understanding, we explore the bias-variance trade-off and look into the effects of training data size and the number of parameters. Watch on:FAU TVFAU TV (no memes)YouTube Read the Transcript (Summer 2020) at...

In this video, we introduce a rank-reduced version of the LDA. Watch on:FAU TVYouTube

This video presents max and average pooling, introduces the concept of fully convolutional networks, and hints on how this is used to build deep networks. Watch on:FAU TVFAU TV (no memes)YouTube Read the Transcript (Summer 2020) at:LMETowards Data Science

This video presents convolutional layers including the concepts of strided and dilated convolutions and how to compute their gradient. Watch on:FAU TVFAU TV (no memes)YouTube Read the Transcript (Summer 2020) at:LMETowards Data Science

In this video, we look into some useful properties of discriminant analyses. Watch on:FAU TVYouTube