Syllabus
Lecture 1

introduction [B2 sc2.1]

linear regression and linear models [B1 sc3.1; B1 sc1.11.4] (In R:
?lm )

gradient descent, NewtonRaphson (batch and sequential) (In R:
?optim )

least squares method [B6, sc5.15.10]

knearest neighbor [B2, 12.4; B3, 3.1.3; B6, 5.15.10]

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, sc1.11.4, sc3.1; B2, sc7.17.3, sc7.107.11]

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 sc7.17.3, sc7.107.11]

logistic regression [B1 sc2.1, ]
Lecture 4

linear models for classification

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]

multilayer perceptrons [B1 sc5.25.3, 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, sc12.112.3.4; B1 sc2.5, sc77.1.5]
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:

kmeans, mixtures models, EM algorithm [B1 sc 9.1,9.2; B2 ch14.114.5]
(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: 20130311 11:04:04 CET
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