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Lecture
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Date
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Topics
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Reading Materials
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Handouts
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1
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April-2
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Introduction to the Machine Learning:
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Alpaydin: Chp 1.
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2013lecture1.pdf
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2
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April-4
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Bayes Decision Theory
Generalization and VC dimension
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Alpaydin: Chp 2.
2.1, 2.2,2.3, Chp 3.
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2013lecture2.pdf
LatexNotes2
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3
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April-9
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ROC and Precision/Recall Curves
Curse of Dimensionality
Bias and Variance
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Same Chps.
Also Chp 4.3,4.4, 4.7,4.8
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2013lecture3.pdf
LatexNotes3
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4
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April-11
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Learning Parametric Distributions
Exponential Models
Sufficeint Statistics
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Chp
4.1,4.2
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2013lecture4.pdf LatexNotes4
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5
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April-16
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Continuation of Previous Lecture
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Previous lecture
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6
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April-18
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Non-Parametric Methods
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Chp 8
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2013lecture5.pdf LatexNotes5
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7
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April-23
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Regression
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Chp 4.6, 5.8
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2013lecture6.pdf
LatexNotes6
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8
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April-25
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AdaBoost
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Chp 6.4,6.5
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2013lecture7.pdf LatexNotes7
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9
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April-30
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Perceptron and Support Vector Machines
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PrimalDual
Chp 10.9
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2013lecture8.pdf LatexNotes8
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10
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May-2
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No Lecture
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11
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May-7
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The
Kernel Trick
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2013lecture9.pdf
LatexNotes9
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12
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May-9
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PCA and Fisher's
LDA
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Chp 6
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2013lecture10.pdf LatexNotes10
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13
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May-14
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Nonlinear Dimension Reduction
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2013lecture11.pdf
LatexNotes11
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14
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May-16
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K-means and EM Algorithm
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Chp 7
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2013lecture12.pdf
LatexNotes12
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15
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May-21
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Decision Trees and Spectral Clustering
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Chp 9
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2013lecture13.pdf LatexNotes13
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16
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May-23
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Probability on Graphs
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Chp 3.7,3.
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2013Lecture14.pdf
LatexNotes14
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17
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May-28
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Hidden markov Models
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Chp 15
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2013Lecture15.pdf
LatexNotes15
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18
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May-30
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SVMs with nmulitle classes and latent
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2013Lecture16.pdf
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19
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June-4
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20
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June-6
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