Lectures

You can download the lectures here. We will try to upload lectures prior to their corresponding classes.

• 

  Introduction Session
  tl;dr: This session is an introduction to the course and the logistics.
  Instructor: Mirerfan Gheibi
  [Slides] [Recorded Session]
  

  Main Topics:

  • Course Format and Logistics
  • Introductory Definitions and Concepts
• 

  Descriptive Statistics and Exploratory Data Analysis
  tl;dr: This lecture covers the basics of descriptive statistics and exploratory data analysis.
  Instructor: Rob Craig
  [Slides] [Recorded Session] [Fortune 500 Dataset]
  

  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.
• 

  Data Cleaning and Cross Validation
  tl;dr: This lecture covers data cleaning and cross validation.
  Instructor: Pius Oyedepo
  [Slides] [Recorded Session] [Google Colab Notebook]
  

  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.
• 

  Regression
  tl;dr: This lecture covers regression.
  Instructor: Yael Petel, PhD
  [Slides] [Recorded Session] [Google Colab Notebook]
  

  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.
• 

  Classification
  tl;dr: This lecture covers classification.
  Instructor: Diana Borovik
  [Slides] [Recorded Session] [Google Colab Notebook]
  

  Main Topics:

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

  Suggested Readings:

  • Chapter 7 of The Orange Book of Machine Learning.
• 

  Ensemble, Hyperparameter Optimization, and Feature Selection
  tl;dr: This lecture covers ensemble methods, hyperparameter optimization, and feature selection.
  Instructor: Mirerfan Gheibi
  [Slides] [Recorded Session] [Google Colab Notebook]
  

  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.
• 

  Explainability
  tl;dr: This lecture covers explainability.
  Instructor: Saeed Garmsiri
  [Slides] [Recorded Session] [Google Colab Notebook]
  

  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