The quiz will cover material through Vector Semantics

Historical figures

Briefly explain who/what these people or algorithms are, relate them to AI.

• Eve Clark
• J. R. Firth
• Yann Le Cun
• Tomas Mikolov

No Calculus details

You will not need to remember/calculate derivatives for specific functions, or composed sets of functions. I want you to know the high-level picture of what derivative computations you're asking a tool (e.g. pytorch) to do for you.

Neural Nets

• Design (i.e. connected set of linear classifiers)
  • Hidden Layers
  • When we have multiple layers, why should there be a (non-linear) activation function between them?
  • What are the advantages of deep networks?
• What kinds of functions can a neural net approximate?
• Training
  • Top-level (aka simple) update equation for a single weight in the network
  • What does backpropagation compute? High-level picture of how it works. E.g. where do we use the chain rule? Why do we need the forward values?
  • Symmetry breaking
  • Data augmentation
  • Regularization
  • Dropout
  • Overfitting
  • Vanishing/exploding gradients
  • Weight regularization
  • Leaky ReLu
  • Why must we initialize weights to random values rather than zero? (You don't need to give names or details for specific initialization methods.)
  • epochs, (mini-)batches
• Convolutional neural networks
  • What is convolution?
  • How does a convolutional layer work?
  • In what situations would we want a convolutional layer vs. a fully-connected layer?
  • Depth and stride
  • What is a pooling layer? Why would max pooling be useful.
  • Overall architecture, e.g. what kinds of features are detected in early vs. late layers?
  • Weight/parameter sharing
• Generative adversarial neural network
• Adversarial examples
  • in computer vision
  • in natural language
• Recurrent neural networks
  • High-level view of how they work
  • When would we compute loss from last unit vs. summed over all units?
  • Bidirectional RNN
  • How does a "Gated RNN" differ from a standard one?

Vector Semantics (Word Embeddings)

• Logic-based vs. context-based representations of meaning
• Principle of contrast
• Vector representations for words
  • one-hot representations for words
  • word embeddings/feature vectors
  • cosine/dot product similarity
  • how do they model analogies, compositional semantics?
  • how to evaluate them
• Building feature vectors
  • relating words to documents
  • relating words to words
• Normalization and smoothing
  • What's wrong with the raw vectors?
  • TF-IDF (don't memorize the exact equation!)
  • PMI and PPMI
  • When would a PMI value be negative? When would negative values be reliable vs just noise?
  • Singular value decomposition (Principal Components Analysis)
• Word2vec
  • Main outline of algorithm
  • Why are words and contexts embedded separately?
  • Negative sampling
  • Sigmoid function (definition, relating probabilities of good and bad)
• Word2vec details
  • Uses more negative examples than positive ones
  • Raising context counts to a power
  • Weighting context words by distance from focus word
  • Deleting rare words, subsampling frequent ones