Slide improvements

[FunnyPhantom]

These slides are getting used as a reference for teaching in the ML for BioInformatic course as well.
In the process of class, some points of improvement got found. This issues tries to serve as a thread for conveying these improvements.

[FunnyPhantom]

In the first slide, the slide that shows the category of MLs have 2 typos that need to be fixed.
First: Recommender system (is written recommended system)
Second: Feature Elimination (is written feature elicitation)

[mahsayazdani]

@FunnyPhantom Thank you for your suggestion. Please provide the exact name of the slide and page numbers.

[FunnyPhantom]

@mahsayazdani the file that needs improvement resides here:
Slides/Chapter_02_Classical_Models/Introduction to ML/Figs/1.jpeg

[FunnyPhantom]

There is another issue in here: https://github.com/asharifiz/Introduction_to_Machine_Learning/blob/3a595142161801b224e9fd06b1e447de7dfb0749/Slides/Chapter_02_Classical_Models/Loss/Loss.tex#L269
The definition of this function should replace cosh with Logcosh.

[FunnyPhantom]

There is also another improvements that can happen.

Ideally after this slide:
https://github.com/asharifiz/Introduction_to_Machine_Learning/blob/a78a9025418769af3d4d99d4246b108cd504eff3/Slides/Chapter_03_Train_and_Evaluation/SVM_ML2022.tex#L554

[FunnyPhantom]

In this slide, there are multiple improvements:
https://github.com/asharifiz/Introduction_to_Machine_Learning/blob/main/Slides/Chapter_04_Tabular_Data_Models/Chapter%204%20(ML_Models_for_Tabular_Datasets).pdf

1. The Majority Voting slide should be after soft clustering.
2. In the Why Majority Voting slide, the ensemble error is missing a choose(n,k).

[FunnyPhantom]

3. for the P(k) in the slide above, the condition of k > [n/2] should be removed. (but not for the ensemble error)

[FunnyPhantom]

On each node, i will make my tree to use random subset of features (sqrt(n))

There is a sentence which is not there iand does not convey the sould of fthe topic.

[FunnyPhantom]

https://github.com/asharifiz/Introduction_to_Machine_Learning/blob/be0f28ab925daae56ef2ae208b97add3cf00441f/Slides/Chapter_05_Deep_Neural_Networks/Introduction-to-NN.tex#L1244

Sample size should be removed in the following picture.

[FunnyPhantom]

The Fanout notation is described here:
https://github.com/asharifiz/Introduction_to_Machine_Learning/blob/be0f28ab925daae56ef2ae208b97add3cf00441f/Slides/Chapter_05_Deep_Neural_Networks/Introduction-to-NN.tex#L1137

It should be added before here:
https://github.com/asharifiz/Introduction_to_Machine_Learning/blob/be0f28ab925daae56ef2ae208b97add3cf00441f/Slides/Chapter_05_Deep_Neural_Networks/Introduction-to-NN.tex#L1223

[FunnyPhantom]

Also for the xavier initialization slides, please denote whether it has been initialized with normal distribution, or uniform distribution.

[FunnyPhantom]

In this figure, the "loss" should be changed to "training loss"
https://github.com/asharifiz/Introduction_to_Machine_Learning/blob/be0f28ab925daae56ef2ae208b97add3cf00441f/Slides/Chapter_05_Deep_Neural_Networks/Introduction-to-NN.tex#LL1627C13-L1627C13

[FunnyPhantom]

In this figure, the "testing error" should be changed to "validation error"
https://github.com/asharifiz/Introduction_to_Machine_Learning/blob/be0f28ab925daae56ef2ae208b97add3cf00441f/Slides/Chapter_05_Deep_Neural_Networks/Introduction-to-NN.tex#L1743