Lectures

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

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  Lecture 1 Course Introduction
  Description: We will go over course policies, a brief description and introduction
  [Notebook]
  
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  Lecture 2 Python I
  Description: We will go over basic commands in python
  [Notebook]
  

  Suggested Readings:

  • Readings 1
  • Readings 2
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  Lecture 3 Python II
  Description: We will discuss computations and visualizations in python
  [Notebook]
  

  Suggested Readings:

  • Readings 1
  • Readings 2
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  Lecture 4 Probability
  Description: We will discuss basic concepts in Probability theory
  [Notebook]
  

  Suggested Readings:

  • Readings 1
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  Lecture 5 Statistical inference
  Description: We will discuss some fundamental theorems in statistics and basics of Bayesian inference
  [Notebook]
  

  Suggested Readings:

  • Readings 1
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  Lecture 6 Regression I
  Description: We will discuss basics of regression and linear estimators
  [Notebook]
  
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  Lecture 7 Regression II
  Description: We will discuss some advanced linear regression models with relevant statistical considerations
  [Notebook]
  
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  Lecture 10 Regression diagnostics
  Description: We will discuss regression diagnostic techniques that are useful for checking model validity
  [Notebook]
  
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  Lecture 11 Confidence Intervals 1
  Description: We will discuss uncertainty and confidence estimation for linear regression model parameters
  [Notebook]
  
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  Lecture 12 Confidence Intervals 2
  Description: We will discuss Confidence Intervals and Prediction Intervals in Linear Regression
  [Notebook]
  
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  Lecture 16 Lasso and Ridge Regression
  Description: We will discuss Ridge Regression and Lasso Regression
  [Notebook]
  
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  Lecture 17 Regression model comparison
  Description: We will compare performance of Linear, Ridge and Lasso Regression on noisy datasets in python
  [Notebook]
  
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  Lecture 18 t-test, p-value, F-test and Rsquared
  Description: We will explore different methods for model selection in linear Regression models
  [Notebook]
  
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  Lecture 19 Cross Validation and Generalized Cross Validation
  Description: We will explore basics of model selection for Ridge Regression models
  [Notebook]
  
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  Lecture 20 K-Fold Cross Validation
  Description: We will explore basics of model selection for complex models such as Lasso
  [Notebook]
  
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  Lecture 21 Classification Introduction
  Description: We will cover basics of classification with logistic regression algorithm
  [Notebook]
  
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  Lecture 22 Logistic Regression
  Description: We will start exploring logistic regression as a way to learn a classification boundary
  [Notebook]
  

  Suggested Readings:

  • Readings 1
  • Readings 2
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  Lecture 23 Logistic Regression 2
  Description: We will delve deeper into the optimization problem for logistic regression and gradient descent algorithm for optimizing this objective
  [Notebook]
  

  Suggested Readings:

  • Readings 1
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  Lecture 24 Regularized Classification
  Description: We will explore various algorithmic regularization strategies for classification via logistic regression algorithm
  [Notebook]
  

  Suggested Readings:

  • Readings 1
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  Lecture 25 Multiclass Probabilistic Classification
  Description: We will explore multinomial logistic regression for multiclass probabilisitic classification
  [Notebook]
  

  Suggested Readings:

  • Readings 1
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  Lecture 29 Multinomial Logistic Regression
  Description: We will explore multinomial logistic regression for multiclass probabilisitic classification
  [Notebook]
  
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  Lecture 30 Classifier evaluation
  Description: We will explore different metrics to evaluate the performance of classifiers
  [Notebook]
  

  Suggested Readings:

  • Readings 1
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  Lecture 31 ROC Curve
  Description: We will discuss ROC curve and Area Under the Curve (AUC) as a measure of classification model performance
  [Notebook]