Exam Prep

The question is separated in 3 parts.

1. Core Problem (The Prerequisite of the course. i.e. Statistics{ Mean, Median, Mode, Histogram}, Python {Generator, }, ML Algorithms {Linear Regression, Logistic Regression,…} )
2. Problem (The slides {The Visualisation, Interpretation, Explanation})
3. Advanced Problem (The math behind the Algorithms)

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Core Problem

The prerequisites:

Statistics

1. Mean
2. Median
3. Mode
4. Standard Deviation
5. Variance
6. Histogram
7. Normal Distribution (kstest)

Python

8. Generator
9. Read CSV file
10. What is Panda, Numpy basic operations
11. Matplotlib and draw a Histogram
12. Sci-kit Learn uses

ML Algorithms

13. Understanding of Linear Algorithms, Linear Polynomial Algorithms…
14. Confusion Matrix Term And types of Error. i.e. Sensitivity, Specificity, Recall, Precision, TPR, FPR, F-1 Score, MCC, ROC-AUC, R2 Error, MSE
15. Algorithms Simplified Understanding

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Problem

1. Introduction

2. Interpretation 1

3. interpretation 2

4. Feature Impact

5a. Global Model-Agnostic Interpretation Method Without Surrogate

5b. Global Model-Agnostic Interpretation Method With Surrogate

6. Local Model-Agnostic Interpretation Method

7. Anchor And Counterfactual Explanation Method

8a. Visualisation of CNN (Convolution, MaxPooling etc)

8b. Visualisation of CNN (Activation, Gradient, Perturbation)

9. Time Series

10. Bias Mitigation

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Advanced Problem