Machine Learning

Next Meeting

• When: Wednesday, 1/12/2010 @ 7:30-9:00pm
• Where: 2169 Mission St. (back corner classroom)
• Topic: Semi-supervised Learning
• Details:
• Presenter: Clay W

Future Talks and Topics

• Neural Network Workshop (Mike S, 1/26/2011)
• Recurrent Neural Networks, Boltzmann Machines (Mike S, February 2011)
• Boosting and Bagging (Thomas, unscheduled)
• CS229 second problem set
• RPy?

Mailing List

https://www.noisebridge.net/mailman/listinfo/ml

Projects

• Fundraising
• Noisebridge Machine Learning Course
• Kaggle Social Network Contest
• KDD Competition 2010
• HIV

Datasets

• UCI Machine Learning Repository
• DataSF.org
• Face Recognition Databases

Software Tools

• OpenCV
  • Computer Vision Library
  • Has ML component (SVM, trees, etc)
  • Online tutorials here
• Mahout
  • Hadoop cluster based ML package.
• Weka
  • a collection of data mining tools and machine learning algorithms.
• MOA (Massive Online Analysis)
  • Offshoot of weka, has all online-algorithms
• scikits.learn
  • Machine learning Python package
• LIBSVM
  • c-based SVM package
• PyML

Presentations and other Materials

• Awesome Machine Learning Applications -- A list of cool applications of ML
• Hands-on Machine Learning, a presentation jbm gave on 2009-01-07.
• http://www.youtube.com/user/StanfordUniversity#g/c/A89DCFA6ADACE599 Stanford Machine Learning online course videos]
• Media:Brief_statistics_slides.pdf, a presentation given on statistics for the machine learning group
• LinkedIn discussion on good resources for data mining and predictive analytics
• Face Recognition Algorithms

Topics to Learn and Teach

CS229 - The Stanford Machine learning Course @ noisebridge

• Supervised Learning
  • Linear Regression
  • Linear Discriminants
  • Neural Nets/Radial Basis Functions
  • Support Vector Machines
  • Classifier Combination [1]
  • A basic decision tree builder, recursive and using entropy metrics

• Unsupervised Learning
  • Hidden Markov Models
  • Clustering: PCA, k-Means, Expectation-Maximization
  • Graphical Modeling
  • Generative Models: gaussian distribution, multinomial distributions, HMMs, Naive Bayes
  • Deep Belief Networks & Restricted Boltzmann Machines

• Reinforcement Learning
  • Temporal Difference Learning

• Math, Probability & Statistics
  • Metric spaces and what they mean
  • Fundamentals of probabilities
  • Decision Theory (Bayesian)
  • Maximum Likelihood
  • Bias/Variance Tradeoff, VC Dimension
  • Bagging, Bootstrap, Jacknife [2]
  • Information Theory: Entropy, Mutual Information, Gaussian Channels
  • Estimation of Misclassification [3]
  • No-Free Lunch Theorem [4]

• Machine Learning SDK's
  • OpenCV ML component (SVM, trees, etc)
  • Mahout a Hadoop cluster based ML package.
  • Weka a collection of data mining tools and machine learning algorithms.

• Applications
  • Collective Intelligence & Recommendation Engines