Lecture Videos

In this video, we talk about conditional GANs and the CycleGAN. Watch on:FAU TVFAU TV (no memes)YouTube Read the Transcript (Summer 2020) at:LMETowards Data Science

In this video, we introduce the "no free lunch" theorem and the bias-variance trade-off. Watch on:FAU TVYouTube

In this video, we talk about the basic ideas of Generative Adversarial Networks (GANs) and show some examples. Watch on:FAU TVFAU TV (no memes)YouTube Read the Transcript (Summer 2020) at:LMETowards Data Science

In this video, we discuss three measures to determine "non-gaussianity". Watch on:FAU TVYouTube

In this video, we show fundamental concepts of autoencoders (AEs) ranging from undercomplete and sparse AEs, over stacked and denoising AEs all the way to Variational Autoencoders. Watch on:FAU TVFAU TV (no memes)YouTube Read the Transcript (Summer 2020) at:LMETowards Data Science

In this video, we discuss the importance of non-gaussianity for the extraction of independent components.  Watch on:FAU TVYouTube

In this video, we introduce the topic of unsupervised learning and start looking at Restricted Boltzmann Machines as one of the first approaches for Unsupervised Deep Learning. Watch on:FAU TVFAU TV (no memes)YouTube Read the Transcript (Summer 2020) at:LMETowards Data Science

In the last video on reinforcement learning, we look into the deep reinforcement learning techniques. We start looking into how Deep Mind beat Atari Games and in particular breakout. Furthermore, we look into the technology behind AlphaGo and AlphaGoZero to play Go, Chess, and Shogi on world-class ...

In this video, we introduce the concept of the independent component analysis. Watch on:FAU TVYouTube

This video discusses several other solution approaches to learning games such as Monte Carlo Techniques, Temporal Difference Learning, Q Learning, and learning universal function approximators for reinforcement learning using the policy gradient. Watch on:FAU TVFAU TV (no memes)YouTube Read t...