CS-498 Applied Machine Learning: Travel Arrangements

Current

1. The federal government wants me 25 Sep, and Allen AI institute on 27 Sep; depending on travel, I may miss 2 Oct lecture, too. It is now certain that I am forced to miss 2 Oct; watch movies below. As a result, I won't be in morning lecture at least 25, 27 Sep and perhaps 2 Oct. However, Trevor will do afternoon lecture as usual. I have attached videos below, dealing with the topics.
   • Clustering using agglomerative and divisive methods
   • Clustering using k-means
   • K-means example
   • Vector Quantization
   • EM: review of multivariate normal distribution
   • EM: mixture models
   • EM: setting up EM for mixtures
   • EM: Calculations for the E-step, I
   • EM: Calculations for the E-step, II
   • EM: Calculations for the M-step
   • EM: Remarks
2. Travel commitments force me to miss lecture on 6 Nov, 8 Nov, and 13 Nov. This is infuriating, because we'll be doing the nicest topic to lecture (Deep Learning). Trevor will do afternoon lecture as usual. I have attached videos below, dealing with the topics.
   • Units - building blocks of neural networks
   • Gradients
   • Backpropagation
   • Convolutional layers
   • More about convolutional layers
   • Tricks to improve gradients
   • Building an image classifier with neural networks
   • Batch normalization and residual networks
   • TSNE - a different approach to multidimensional scaling
   • Autoencoders
   • Variational autencoders
   • Generative adversarial networks