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

  • Lecture 1 Course Introduction
    Description: We will go over course policies, a brief description and introduction
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  • Lecture 2 Python I
    Description: We will go over basic commands in python
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  • Lecture 3 Python II
    Description: We will discuss computations and visualizations in python
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  • Lecture 4 Probability
    Description: We will discuss basic concepts in Probability theory
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  • Lecture 5 Statistical inference
    Description: We will discuss some fundamental theorems in statistics and basics of Bayesian inference
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  • Lecture 6 Regression I
    Description: We will discuss basics of regression and linear estimators
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  • Lecture 7 Regression II
    Description: We will discuss some advanced linear regression models with relevant statistical considerations
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  • Lecture 10 Regression diagnostics
    Description: We will discuss regression diagnostic techniques that are useful for checking model validity
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  • Lecture 11 Confidence Intervals 1
    Description: We will discuss uncertainty and confidence estimation for linear regression model parameters
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  • Lecture 12 Confidence Intervals 2
    Description: We will discuss Confidence Intervals and Prediction Intervals in Linear Regression
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  • Lecture 16 Lasso and Ridge Regression
    Description: We will discuss Ridge Regression and Lasso Regression
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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
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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
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  • Lecture 19 Cross Validation and Generalized Cross Validation
    Description: We will explore basics of model selection for Ridge Regression models
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  • Lecture 20 K-Fold Cross Validation
    Description: We will explore basics of model selection for complex models such as Lasso
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  • Lecture 21 Classification Introduction
    Description: We will cover basics of classification with logistic regression algorithm
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  • Lecture 22 Logistic Regression
    Description: We will start exploring logistic regression as a way to learn a classification boundary
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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
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  • Lecture 24 Regularized Classification
    Description: We will explore various algorithmic regularization strategies for classification via logistic regression algorithm
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  • Lecture 25 Multiclass Probabilistic Classification
    Description: We will explore multinomial logistic regression for multiclass probabilisitic classification
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  • Lecture 29 Multinomial Logistic Regression
    Description: We will explore multinomial logistic regression for multiclass probabilisitic classification
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  • Lecture 30 Classifier evaluation
    Description: We will explore different metrics to evaluate the performance of classifiers
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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
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