classif part2

including errata
slds-lmu · Nov 9, 2024 · 082ca41 · 082ca41
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diff --git a/content/appendix/02_errata.md b/content/appendix/02_errata.md
@@ -7,13 +7,11 @@ title: Errata
 - Chapter 1.4 (Models & Parameters) - slide 5/10: d-dimensional vector, not p-dimensional
 - Chapter 2.1 (Regression losses): Slide 1/5 sign in bullet point 4
 - Chapter 2.2 (Deep Dive OLS): Slide 2/2 last lines in left column
+- Chapter 3.6 (Naive Bayes): Slide 3/6: Missing exponents in figure
 - Chapter 4.3 (Simple Measures for Classification) - slide 6/9: Error in cost matrix
 - Chapter 4.4 (Perfomance Evaluation: Test Error) - slide 8/13: The variance of MSE decreases with test set size, not the mean of MSE
 - Chapter 4.7 (Classification measures): Slide 6/9 cost computation
 - Chapter 6.2 (CART: Growing a Tree) - slide 5/8: Wrong plot is displayed in video (axis wrong, points missing)
-- Chapter 7.2 (Forests: Intro) - slides 7/8 and 8/8: Error in OOB error
-- Chapter 7.4 (Forests: Feature importance) - slide 3/3: Error in permutation based variable importance
-- Chapter 7.4 (Forests: Feature importance) - new slide 4/4: Improve explanation of MeanDecreaseAccuracy and MeanDecreaseGini
 - Chapter 11.6 (0-1 Loss): Slides 2/5 and 4/5 Errors in notation of conditional probability inside of expectation
 - Chapter 11.7 (Bernoulli Loss): Slides 9/10 and 10/10 Errors in Bernoulli Loss and Entropy Splitting Criterion
 - Chapter 11.12 (MLE2): Slide 2/5 wrong negative sign in NLL equation

diff --git a/content/chapters/03_supervised_classification/03-03-classification-linear.md b/content/chapters/03_supervised_classification/03-03-classification-linear.md
@@ -9,7 +9,7 @@ Linear classifiers are an essential subclass of classification models. This sect
 
 ### Lecture video
 
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 ### Lecture slides
 

diff --git a/...ent/chapters/03_supervised_classification/03-05-classification-discranalysis.md b/...ent/chapters/03_supervised_classification/03-05-classification-discranalysis.md
@@ -9,7 +9,7 @@ Discriminant analysis is a generative approach toward constructing a classifier.
 
 ### Lecture video 
 
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 ### Lecture slides
 

diff --git a/content/chapters/03_supervised_classification/03-06-classification-naivebayes.md b/content/chapters/03_supervised_classification/03-06-classification-naivebayes.md
@@ -9,7 +9,7 @@ Naive Bayes is a generative approach based on an assumption of conditional indep
 
 ### Lecture video 
 
-{{< video id="bvAYZsIt04U" >}}
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 ### Lecture slides