Syllabus
Lecture 1
-
introduction [B2 sc2.1]
-
linear regression and linear models [B1 sc3.1; B1 sc1.1-1.4] (In R:
?lm )
-
gradient descent, Newton-Raphson (batch and sequential) (In R:
?optim )
-
least squares method [B6, sc5.1-5.10]
-
k-nearest neighbor [B2, 1-2.4; B3, 3.1.3; B6, 5.1-5.10]
-
curse of dimensionality [B1 sc1.4]
Lecture 2
-
regularized least squares (aka, shrinkage or ridge regr.) [B1 sc3.1.4]
-
locally weighted linear regression [B2, sc6.1.1]
-
probability theory [B2 sc1.2]
-
probability interpretation [B1, sc1.1-1.4, sc3.1; B2, sc7.1-7.3, sc7.10-7.11]
-
maximum likelihood approach [B1 sc1.2.5]
-
Bayesian approach and application in linear regression [B1 sc1.2.6, 2.3, 3.3, ex. 3.8]
Lecture 3
-
probabilistic approach to learn parameters of binary variables [B2 sc2.1]
-
model assessment [B1 sc1.5.5; sc3.2; B2 sc7.1-7.3, sc7.10-7.11]
-
logistic regression [B1 sc2.1, ]
Lecture 4
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linear models for classification
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multinomial (logistic) regression [B1 sc2.2]
Lecture 5
-
generalized linear models [B1 sc2.4] (In R:
?glm )
-
decision theory [B1 sc1.5]
Lecture 6
-
neural networks
-
perceptron algorithm [B1 5.1]
-
multi-layer perceptrons [B1 sc5.2-5.3, sc5.5; B2 ch11] (in R:
library(nnet); ?nnet )
Lecture 7
-
generative algorithms
-
Gaussian discriminant analysis [B1 sc4.2] (in R:
library(MASS); ?lda, ?plot.lda )
-
naive Bayes (in R:
library(e1071); ?naiveBayes )
Lecture 8 and 9
-
support vector machines and kernel methods [B2 sc2.8.2, ch6, sc12.1-12.3.4; B1 sc2.5, sc7-7.1.5]
Lecture 10
-
Practical Advice [B12]
-
Learning Theory [B12]
Lecture 11
-
probabilistic graphical models
-
Discrete [B1 sc8.1]
-
Linear Gaussian [B1 sc8.1]
-
Mixed Variables
-
Conditional Independence [B1 sc8.2, wikipedia]
Lecture 12
-
probabilistic graphical models, Inference
-
Exact in Chains and Polytrees [B1, sc 8.4]
-
Approximate [B4 sc14.5]
Lecture 13
-
Unsupervised Learning:
-
k-means, mixtures models, EM algorithm [B1 sc 9.1,9.2; B2 ch14.1-14.5]
(in R
kmeans , em from mclust package)
Lecture 14
-
tree based methods [B1 sc1.6, 14.4; B2 sc9.2] (in R
rpart from
rpart package and ctree from party package)
-
principal component analysis [B10; B1 sc12.1; B2 sc14.5.1] (in R
princomp )
Author: Marco Chiarandini
<marco@imada.sdu.dk>
Date: 2013-03-11 11:04:04 CET
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