# DM825 - Introduction to Machine Learning

## 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]
• regularized least squares (aka, shrinkage or ridge regr.) [B1 sc3.1.4]
• locally weighted linear regression [B2, sc6.1.1]
• model selection [B1 sc1.3; sc3.1; B2 sc7.1-7.3, sc7.10-7.11]

#### Lecture 2

• 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

• linear models for classification
• logistic regression [B1 sc2.1, ]
• multinomial (logistic) regression [B1 sc2.2]
• generalized linear models [B1 sc2.4] (In R: ?glm)
• decision theory [B1 sc1.5]

#### Lecture 4

• 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 5

• generative algorithms
• Gaussian discriminant analysis [B1 sc4.2]
• naive Bayes (in R: library(e1071); ?naiveBayes)

#### Lecture 6

• linear methods for classification [B2 ch4]
• linear regressions of indicator function via least squares
• logistic regression (max conditional likelighood)
• Gaussian discriminant and linear discrimninant analysis (in R: library(MASS); ?lda, ?plot.lda)
• perceptron
• optimal separating hyperplanes (in R: library(e1071); ?svm)

#### Lecture 7

• kernels and support vector machines [B2 sc2.8.2, ch6, sc12.1-12.3.4; B1 sc2.5, sc7-7.1.5]

#### Lecture 8

• learning theory [B1 sc1.6, sc7.1.5]

#### Lecture 9

• probabilistic graphical models
• Discrete [B1 sc8.1]
• Linear Gaussian [B1 sc8.1]
• Mixed Variables
• Conditional Independence [B1 sc8.2, wikipedia]

#### Lecture 10

• probabilistic graphical models, Markov Random Fields [B1 sc8.3]

#### Lecture 11

• probabilistic graphical models, Inference
• Exact
• Chains [B1 sc8.4]
• Polytree [B1 sc8.4]
• Approximate [B4 sc14.5]

#### Lecture 12

• k-means, mixtures models, EM algorithm [B1 ch9; B2 ch14.1-14.5]
• hidden Markov models [B1 sc13.1]

#### Lecture 13

• bagging, boosting [B1 sc14.1-14.3]
• tree based methods [B1 sc1.6, 14.4; B2 sc9.2]

Date: 2011-07-29 11:19:03 CEST

HTML generated by org-mode 6.21b in emacs 23