Schedule

Main Topics:

• Course Format and Logistics
• Introductory Definitions and Concepts

Main Topics:

• Mean, median, mode, etc.
• Variance, standard deviation, etc.
• Guassian distribution
• Chebyshev’s inequality
• Skewness and kurtosis
• Data Quality
• DataFrames
• Visualization, Charts and Graphs
• Outliers
• Correlation Coefficient

Suggested Readings:

• Chapter 2 and 3 of The Orange Book of Machine Learning.

Main Topics:

• Missing Values
• Outliers
• Data De-duplication
• Feature Encoding
• Train test split
• Cross validation
• Data Leakage
• Covariate Shift

Suggested Readings:

• Chapter 4 and 5 of The Orange Book of Machine Learning.

Main Topics:

• Regression Basics
• Linear Regression
• Polynomial Regression
• Metrics
• Overfitting and Underfitting
• Conformal Prediction

Suggested Readings:

• Chapter 6 of The Orange Book of Machine Learning.

Main Topics:

• Logistic Regression
• Logloss function
• Metrics
• Imbalanced classification
• Decision Trees

Suggested Readings:

• Chapter 7 of The Orange Book of Machine Learning.

Main Topics:

• Random Forest
• AdaBoost
• Gradient Boosting
• Hyperparameter Optimization
• Feature Selection
• Feature Engineering
• PCA

Suggested Readings:

• Chapter 8, 9, 10, and 11 of The Orange Book of Machine Learning.

Main Topics:

• SHAP
• LIME
• Generative AI Explainability
• Transparent AI

Suggested Readings:

• Demystifying the Black Box: Advanced Techniques in Interpretable AI with SHAP and LIME
• Unraveling the Black Box: Explainability in Generative AI — Part 1
• Illuminating the Black Box: Integrating Explainability Techniques for Transparent AI