6.867 Machine learning, lecture 7 (Jaakkola)
1
Lecture topics:
Kernel form of linear regression
Kernels, examples, construction, properties
Linear regression and kernels
Consider a slightly simpler
6.867 Machine learning, lecture 9 (Jaakkola)
1
Lecture topics:
Kernel optimization
Model (kernel) selection
Kernel optimization
Whether we are interested in (linear) classication or regression we ar
6.867 Machine learning, lecture 10 (Jaakkola)
1
Lecture topics: model selection criteria
Structural risk minimization, example derivation
Bayesian score, Bayesian Information Criterion (BIC)
Model s
6.867 Machine learning, lecture 14 (Jaakkola)
1
Lecture topics:
margin and generalization
linear classiers
ensembles
mixture models
Margin and generalization: linear classiers
As we increase the n
6.867 Machine learning, lecture 13 (Jaakkola)
1
Lecture topics:
Boosting, margin, and gradient descent
complexity of classiers, generalization
Boosting
Last time we arrived at a boosting algorithm f
6.867 Machine learning, lecture 16 (Jaakkola)
1
Lecture topics:
Mixture of Gaussians (contd)
The EM algorithm: some theory
Additional mixture topics
regularization
stage-wise mixtures
conditiona
6.867 Machine learning, lecture 17 (Jaakkola)
1
Lecture topics:
Mixture models and clustering, k-means
Distance and clustering
Mixture models and clustering
We have so far used mixture models as exi
6.867 Machine learning, lecture 8 (Jaakkola)
1
Lecture topics:
Support vector machine and kernels
Kernel optimization, selection
Support vector machine revisited
Our task here is to rst turn the sup
6.867 Machine learning, lecture 6 (Jaakkola)
1
Lecture topics:
Active learning
Non-linear predictions, kernels
Active learning
We can use the expressions for the mean squared error to actively selec
6.867 Machine learning, lecture 5 (Jaakkola)
1
Linear regression, active learning
We arrived at the logistic regression model when trying to explicitly model the uncertainty
about the labels in a line
6.867 Machine learning, lecture 20 (Jaakkola)
1
Lecture topics:
Hidden Markov Models (contd)
Hidden Markov Models (contd)
We will continue here with the three problems outlined previously. Consider h
6.867 Machine learning, lecture 19 (Jaakkola)
1
Lecture topics:
Markov chains (contd)
Hidden Markov Models
Markov chains (contd)
In the context of spectral clustering (last lecture) we discussed a r
6.867 Machine learning, lecture 23 (Jaakkola)
1
Lecture topics:
Markov Random Fields
Probabilistic inference
Markov Random Fields
We will briey go over undirected graphical models or Markov Random F
6.867 Machine learning, lecture 22 (Jaakkola)
1
Lecture topics:
Learning Bayesian networks from data
maximum likelihood, BIC
Bayesian, marginal likelihood
Learning Bayesian networks
There are two p
6.867 Machine learning, lecture 1 (Jaakkola)
1
Example
Lets start with an example. Suppose we are charged with providing automated access
control to a building. Before entering the building each perso
6.867 Machine learning, lecture 2 (Jaakkola)
1
Perceptron, convergence, and generalization
Recall that we are dealing with linear classiers through origin, i.e.,
f (x; ) = sign T x
(1)
where Rd specie
6.867 Machine learning, lecture 21 (Jaakkola)
Lecture topics:
Bayesian networks
Bayesian networks
Bayesian networks are useful for representing and using probabilistic information. There
are two parts
6.867 Machine learning, lecture 3 (Jaakkola)
1
The Support Vector Machine
So far we have used a reference assumption that there exists a linear classier that has
a large geometric margin, i.e., whose
6.867 Machine learning, lecture 4 (Jaakkola)
1
The Support Vector Machine and regularization
We proposed a simple relaxed optimization problem for nding the maximum margin sep
arator when some of the
6.867 Machine learning, lecture 18 (Jaakkola)
1
Lecture topics:
Spectral clustering, random walks and Markov chains
Spectral clustering
Spectral clustering refers to a class of clustering methods tha