NBML Course

Noisebridge Machine Learning Course

We're trying to come up with a hands-on curriculum for teaching Machine Learning at Noisebridge. Please help out in any way you can, such as:

1. Volunteer to teach a course in one of the subjects below
2. Fill in one of the subjects below with links to learning material and related software
3. Show up to classes and ask questions
4. Join the ML Mailing List and talk about stuff
5. Don't talk shit on mathematics - it wants to be your friend!

Online Machine Learning Courses

• Stanford CS229
• MIT OCW 6.867

Curriculum

Machine Learning

• The data
• The model
  • Discriminative vs Generative Models
• Unsupervised vs. Supervised Learning
• Training a Model
  • Maximum Likelihood
  • Optimization
    • Gradient Descent
    • Lagrange Optimization
    • Expectation Maxmimization
  • Overfitting and Regularization
  • Bias-Variance Tradeoff

Linear Regression

• Least Squares Formulation
• Maximum Likelihood Formulation
• Regularization
  • Ridge Regression (L2)
  • Lasso Regression (L1)
  • Least-angle/Elastic Net Regression
• Bayesian Linear Regression

Linear Classification

• Fisher's Linear Discriminant
• Logistic Regression
• Probit Regression

Neural Networks

• Feedforward Nets
• Hopfield Nets/Autoassociators
• Recurrent Nets/Boltzmann Machines
• Deep Belief Nets

Clustering and Dimensional Reduction

• K-Means Clustering
• Principle Component Analysis
• Independent Component Analysis
• Dimensional Reduction and Clustering for Visualization
  • Self Organizing Map (algebraic perspective)
  • Supervised Methods and Refinement (LVQ)
• Clustering Techniques for Text Collections
  • Spherical K-Means
  • Word Sense Disambiguation (Sense Clusters)
  • Latent Semantic Indexing (LSI)
    • Keyword Relatedness Clustering (Semantic Engine)
  • Text Clustering with Self Organizing Map (WebSOM)

Graphical Models

• Bayesian Networks
• Markov Random Fields

Other Perspectives

• Linguistics and The Role of Language
  • Symbolic Methods and Machine Understanding
• Simulation and Integrated Software Systems
  • Autonomous Agents and Evolutionary (Learning) Algorithms

The Fundamentals: Basic Math

Note: it's not essential to understand everything in this section! But the more you learn, the more things will make sense. Wikipedia is your friend.

• Linear Algebra
  • Vectors and Matrices
  • Solving Linear Systems
    • LU Decomposition
  • Vector Spaces
  • Orthogonalization algorithms
  • Eigenvectors and Eigenvalues
  • Quadratic Forms
  • Singular Value Decompostion (SVD)
• Calculus
  • Derivatives, Gradients, and Hessians
  • Integration
  • Fourier Transform
  • Vector Calculus
    • Optimization, Duality, Lagrange Multipliers and Kuhn-Tucker Theorem
• Probability Theory
  • Basic Probability
    • Bayes Theorem
  • Distribution and Density Functions
    • Discrete Distributions
    • Continuous Distributions
  • Random Variables and Vectors
  • Expectation
  • Variance and Covariance
  • Correlation Functions
  • Law of Large Numbers
  • Information Theory
    • Entropy
    • Relative Entropy
    • Mutual Information
• Geometry for Computer Vision and Simulated Environments
• Logic and Set Theory
  • Fuzzy Logic and Control Theory