Download Final Review

yes no Was this document useful for you?
   Thank you for your participation!

* Your assessment is very important for improving the work of artificial intelligence, which forms the content of this project

Document related concepts

Nonlinear dimensionality reduction wikipedia, lookup

Mixture model wikipedia, lookup

Final Review
7-Text Mining
• Unstructured Data
• Two modes of mining: analysis vs. retrieval
• Precision vs. Recall as metric
– With lots of data you can find anything
• Tools for text mining
– Stopwords, stemming
– Term document matrix
• Latent Semantic Indexing (LSI)
– Uses PCA to find ‘concepts’ (topics)
– Documents that share concepts will be close
• Probabilistic Models
– Naïve Bayes vs. Multinomial
• LDA: Documents from Topics from Words
8-Web Mining
• Detecting robots
• Markov Models for Page prediction
• Ranking web pages
– Flow model
– Power iteration
– Random walk and the stationary distribution
• Spider traps and how to get around them
• Adwords model for advertising cost-per-click
9-Advanced Classification
• Neural Networks
Neuron: inputs, linear combination, activation function, output
Architecture: layers, nodes per layer
Training through back propagation
Good for complex problems like face detection, speech, video
• Support Vector Machines
Assume classes are separable
Plus/minus plane, margin, support vectors
Finds the maximum margin separable classifier
If not separable, use “kernel trick”
• Ensemble Methods
Collections of ‘small’ models can fit something complex
Typically beats individual models
Model Averaging
Boosting – fit to models with error upweighted
Bagging – fit to bootstrapped versions of data
Random Forests – fit to trees with random variables at
each split
11-Bayesian Methods
• Hierarchical Modelling with MCMC
No pooling vs complete pooling vs. Bayesian solution
Priors tell how much you should depend on the data
Congugate priors (e..g beta/binomial) make life easy.
MCMC for other cases
Metropolis Hastings: sample from the posterior
Use trace plots to assess convergence
12-Recommender Systems
• Netflix Prize
– We won!
• Recommender Systems
Evaluation via RMSE or DCG
Nearest Neighbors
Ensembles (of teams, of models) very powerful
• Nodes and edges
– Node and edge centrality
– Degrees and degree distribution
• Network Models
Preferential Attachment
Power Law graphs
Small world networks