Machine Learning: Difference between revisions
ThomasLotze (talk | contribs) |
ThomasLotze (talk | contribs) No edit summary |
||
Line 1: | Line 1: | ||
=== Next Meeting=== | === Next Meeting=== | ||
*When: Wednesday, | *When: Wednesday, 5/5/2010 @ 8:00pm | ||
*Where: 2169 Mission St. | *Where: 2169 Mission St. (back corner classroom) | ||
*Topic: | *Topic: A Brief Tour of Statistics | ||
*Presenter: | *Presenter: Thomas | ||
=== Topics to Learn and Teach === | === Topics to Learn and Teach === | ||
Line 57: | Line 56: | ||
=== Notes from Meetings === | === Notes from Meetings === | ||
[[Machine Learning Meetup Notes: 2010-04-28]] -- SVMs | |||
[[Machine Learning Meetup Notes: 2010-04-21]] -- Linear Regression | [[Machine Learning Meetup Notes: 2010-04-21]] -- Linear Regression | ||
[[Machine Learning Meetup Notes: 2010-04-14]] -- (re)Starting new Machine Learning Meetup! | [[Machine Learning Meetup Notes: 2010-04-14]] -- (re)Starting new Machine Learning Meetup! | ||
[[Machine Learning Meetup Notes: 2009-04-01]] -- Finally moving on up: fully-connected backpropagation networks. | [[Machine Learning Meetup Notes: 2009-04-01]] -- Finally moving on up: fully-connected backpropagation networks. |
Revision as of 21:29, 28 April 2010
Next Meeting
- When: Wednesday, 5/5/2010 @ 8:00pm
- Where: 2169 Mission St. (back corner classroom)
- Topic: A Brief Tour of Statistics
- Presenter: Thomas
Topics to Learn and Teach
- Supervised Learning
- Linear Regression (Mike S volunteered to teach)
- 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
- Clustering/PCA
- k-Means Clustering
- Graphical Modeling
- Generative Models: gaussian distribution, multinomial distributions, HMMs, Naive Bayes
- 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
- Applications
- Collective Intelligence & Recommendation Engines
Possible Projects
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.
Notes from Meetings
Machine Learning Meetup Notes: 2010-04-28 -- SVMs
Machine Learning Meetup Notes: 2010-04-21 -- Linear Regression
Machine Learning Meetup Notes: 2010-04-14 -- (re)Starting new Machine Learning Meetup!
Machine Learning Meetup Notes: 2009-04-01 -- Finally moving on up: fully-connected backpropagation networks.
Machine Learning Meetup Notes: 2009-03-25 -- We made perceptrons - added sigmoid, etc.
Machine Learning Meetup Notes: 2009-03-18 -- We made perceptrons - linear function support!
Machine Learning Meetup Notes: 2009-03-11 -- We made perceptrons!
Machine Learning Meetup Notes: 2009-03-04 -- Josh gave a presentation on SVMs
(time is missing!)
Machine Learning Meetup Notes: 2009-02-11 -- Josh gave a presentation on clustering, donuts!
Machine Learning Meetup Notes: 2009-02-04 -- Free-form hang out night, punch and pie
Machine Learning Meetup Notes: 2009-01-28 -- Praveen talked about Neural networks
Machine Learning Meetup Notes: 2008-01-21 -- Jean gave a quick overview of machine learning stuff
Machine Learning Meetup Notes: 2009-01-14 -- Ian gave a talk on k-Nearest Neighbor
Machine Learning Meetup Notes: 2009-01-07 -- Josh did a quick intro to ML presentation