Lectures
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
[Notebook]
-
Lecture 2 Python I
Description: We will go over basic commands in python
[Notebook]
Suggested Readings:
-
Lecture 3 Python II
Description: We will discuss computations and visualizations in python
[Notebook]
Suggested Readings:
-
Lecture 4 Probability
Description: We will discuss basic concepts in Probability theory
[Notebook]
Suggested Readings:
-
Lecture 5 Statistical inference
Description: We will discuss some fundamental theorems in statistics and basics of Bayesian inference
[Notebook]
Suggested Readings:
-
Lecture 6 Regression I
Description: We will discuss basics of regression and linear estimators
[Notebook]
-
Lecture 7 Regression II
Description: We will discuss some advanced linear regression models with relevant statistical considerations
[Notebook]
-
Lecture 10 Regression diagnostics
Description: We will discuss regression diagnostic techniques that are useful for checking model validity
[Notebook]
-
Lecture 11 Confidence Intervals 1
Description: We will discuss uncertainty and confidence estimation for linear regression model parameters
[Notebook]
-
Lecture 12 Confidence Intervals 2
Description: We will discuss Confidence Intervals and Prediction Intervals in Linear Regression
[Notebook]
-
Lecture 16 Lasso and Ridge Regression
Description: We will discuss Ridge Regression and Lasso Regression
[Notebook]
-
Lecture 17 Regression model comparison
Description: We will compare performance of Linear, Ridge and Lasso Regression on noisy datasets in python
[Notebook]
-
Lecture 18 t-test, p-value, F-test and Rsquared
Description: We will explore different methods for model selection in linear Regression models
[Notebook]
-
Lecture 19 Cross Validation and Generalized Cross Validation
Description: We will explore basics of model selection for Ridge Regression models
[Notebook]
-
Lecture 20 K-Fold Cross Validation
Description: We will explore basics of model selection for complex models such as Lasso
[Notebook]
-
Lecture 21 Classification Introduction
Description: We will cover basics of classification with logistic regression algorithm
[Notebook]
-
Lecture 22 Logistic Regression
Description: We will start exploring logistic regression as a way to learn a classification boundary
[Notebook]
Suggested Readings:
-
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:
-
Lecture 24 Regularized Classification
Description: We will explore various algorithmic regularization strategies for classification via logistic regression algorithm
[Notebook]
Suggested Readings:
-
Lecture 25 Multiclass Probabilistic Classification
Description: We will explore multinomial logistic regression for multiclass probabilisitic classification
[Notebook]
Suggested Readings:
-
Lecture 29 Multinomial Logistic Regression
Description: We will explore multinomial logistic regression for multiclass probabilisitic classification
[Notebook]
-
Lecture 30 Classifier evaluation
Description: We will explore different metrics to evaluate the performance of classifiers
[Notebook]
Suggested Readings:
-
Lecture 31 ROC Curve
Description: We will discuss ROC curve and Area Under the Curve (AUC) as a measure of classification model performance
[Notebook]