https://harvard-iacs.github.io/2021-CS109A/pages/materials.html

[logo]

CS109A

  * Syllabus
  * Schedule
  * Materials
  * FAQ
  * Preparation
    [                    ]

Resources

  * labs
  * lectures
  * sections

---------------------------------------------------------------------

Topics

AdaBoost

  * Lecture 21: AdaBoost
  * Lecture 21: AdaBoost [Notebook]

ADD TAGS HERE

  * Lab 05:
  * Lab 07:
  * Lab 08:
  * Lab 09:
  * Lab 10:
  * Lab 11:

Aggregate

  * Lecture 18: Random Forest
  * Lecture 18: Random Forest [Notebook]
  * Lecture 18: Random Forest [Notebook]
  * Lecture 18: Random Forest [Notebook]

Bagging

  * Lecture 18: Random Forest
  * Lecture 18: Random Forest [Notebook]
  * Lecture 18: Random Forest [Notebook]
  * Lecture 18: Random Forest [Notebook]
  * Lecture 17: Bagging
  * Lecture 17: Bagging [Notebook]
  * Lecture 17: Bagging [Notebook]
  * Lecture 17: Bagging [Notebook]

Bayes' Theorem

  * Lecture 15: Logistic Regression II
  * Lecture 15: Logistic Regression II [Notebook]
  * Lecture 15: Logistic Regression II [Notebook]

Bias

  * Lecture 6: Regularization Ridge and Lasso Regression
  * Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
  * Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
  * Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
  * Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
  * Lecture 6: Regularization Ridge and Lasso Regression [Notebook]

big data

  * Lab 06: Principal Components Analysis (PCA)

Binary Response

  * Lecture 15: Logistic Regression II
  * Lecture 15: Logistic Regression II [Notebook]
  * Lecture 15: Logistic Regression II [Notebook]
  * Lecture 14: Logistic Regression I
  * Lecture 14: Logistic Regression I [Notebook]

Binomial

  * Lecture 7: Probability
  * Lecture 7: Probability [Notebook]
  * Lecture 7: Probability [Notebook]

Bootstrap

  * Lecture 18: Random Forest
  * Lecture 18: Random Forest [Notebook]
  * Lecture 18: Random Forest [Notebook]
  * Lecture 18: Random Forest [Notebook]

Bootstrapping

  * Lecture 8: Inference in Regression and Hypothesis Testing
  * Lecture 8: Inference in Regression and Hypothesis Testing 
    [Notebook]
  * Lecture 8: Inference in Regression and Hypothesis Testing 
    [Notebook]
  * Lecture 8: Inference in Regression and Hypothesis Testing 
    [Notebook]
  * Lecture 8: Inference in Regression and Hypothesis Testing 
    [Notebook]
  * Lecture 8: Inference in Regression and Hypothesis Testing 
    [Notebook]

CART

  * Lecture 19: Random Forrest II
  * Lecture 19: Random Forrest II [Notebook]

Case Study

  * Lecture 22: Working Example
  * Lecture 22: Working Example [Notebook]

Classification boundaries

  * Lecture 15: Logistic Regression II
  * Lecture 15: Logistic Regression II [Notebook]
  * Lecture 15: Logistic Regression II [Notebook]

clustering

  * Lab 06: Principal Components Analysis (PCA)

Comparison of Models

  * Lecture 20: Boosting, Gradient Boosting
  * Lecture 20: Boosting, Gradient Boosting [Notebook]

Confidence Intervals

  * Lecture 8: Inference in Regression and Hypothesis Testing
  * Lecture 8: Inference in Regression and Hypothesis Testing 
    [Notebook]
  * Lecture 8: Inference in Regression and Hypothesis Testing 
    [Notebook]
  * Lecture 8: Inference in Regression and Hypothesis Testing 
    [Notebook]
  * Lecture 8: Inference in Regression and Hypothesis Testing 
    [Notebook]
  * Lecture 8: Inference in Regression and Hypothesis Testing 
    [Notebook]

Constant Variance

  * Lecture 8: Inference in Regression and Hypothesis Testing
  * Lecture 8: Inference in Regression and Hypothesis Testing 
    [Notebook]
  * Lecture 8: Inference in Regression and Hypothesis Testing 
    [Notebook]
  * Lecture 8: Inference in Regression and Hypothesis Testing 
    [Notebook]
  * Lecture 8: Inference in Regression and Hypothesis Testing 
    [Notebook]
  * Lecture 8: Inference in Regression and Hypothesis Testing 
    [Notebook]

Content

  * Lecture 1: Introduction to CS109A

Cross Validation

  * Lecture 5: Model Selection and Cross Validation
  * Lecture 5: Model Selection and Cross Validation [Notebook]
  * Lecture 5: Model Selection and Cross Validation [Notebook]
  * S-Section 04: Regularization and Model Selection

Data Plotting

  * Lecture 3: Introduction to Regression kNN and Linear Regression
  * Lecture 3: Introduction to Regression kNN and Linear Regression 
    [Notebook]
  * Lecture 3: Introduction to Regression kNN and Linear Regression 
    [Notebook]
  * Lecture 3: Introduction to Regression kNN and Linear Regression 
    [Notebook]
  * Lecture 3: Introduction to Regression kNN and Linear Regression 
    [Notebook]
  * Lecture 3: Introduction to Regression kNN and Linear Regression 
    [Notebook]

Decision Boundaries

  * Lecture 16: Decision Trees
  * Lecture 16: Decision Trees [Notebook]

Decision Trees

  * Lecture 17: Bagging
  * Lecture 17: Bagging [Notebook]
  * Lecture 17: Bagging [Notebook]
  * Lecture 17: Bagging [Notebook]
  * Lecture 16: Decision Trees
  * Lecture 16: Decision Trees [Notebook]

dimensionality reduction

  * Lab 06: Principal Components Analysis (PCA)

EDA

  * Lecture 12: Visualization
  * Lecture 12: Visualization [Notebook]

Effective Visualization

  * Lecture 12: Visualization
  * Lecture 12: Visualization [Notebook]

ensemble methods

  * Lecture 19: Random Forrest II
  * Lecture 19: Random Forrest II [Notebook]

Entropy

  * Lecture 16: Decision Trees
  * Lecture 16: Decision Trees [Notebook]

Error

  * Lecture 6: Regularization Ridge and Lasso Regression
  * Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
  * Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
  * Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
  * Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
  * Lecture 6: Regularization Ridge and Lasso Regression [Notebook]

EthiCS

  * Lecture 13: EthiCS

explained variance

  * Lab 06: Principal Components Analysis (PCA)

Exponential Loss

  * Lecture 21: AdaBoost
  * Lecture 21: AdaBoost [Notebook]

F-score

  * Lecture 19: Random Forrest II
  * Lecture 19: Random Forrest II [Notebook]

Feature Scaling

  * Lecture 4: Multi-linear and Polynomial Regression
  * Lecture 4: Multi-linear and Polynomial Regression [Notebook]
  * Lecture 4: Multi-linear and Polynomial Regression [Notebook]
  * Lecture 4: Multi-linear and Polynomial Regression [Notebook]

Generalization

  * Lecture 6: Regularization Ridge and Lasso Regression
  * Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
  * Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
  * Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
  * Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
  * Lecture 6: Regularization Ridge and Lasso Regression [Notebook]

Gini Index

  * Lecture 16: Decision Trees
  * Lecture 16: Decision Trees [Notebook]

Gradient Boosting

  * Lecture 20: Boosting, Gradient Boosting
  * Lecture 20: Boosting, Gradient Boosting [Notebook]
  * Lecture 19: Random Forrest II
  * Lecture 19: Random Forrest II [Notebook]

Gradient Descent

  * Lecture 20: Boosting, Gradient Boosting
  * Lecture 20: Boosting, Gradient Boosting [Notebook]

Graphical Integrity

  * Lecture 12: Visualization
  * Lecture 12: Visualization [Notebook]

Help

  * Lecture 1: Introduction to CS109A

High Dimensionality

  * Lecture 10: Principal Component Analysis
  * Lecture 10: Principal Component Analysis [Notebook]
  * Lecture 10: Principal Component Analysis [Notebook]
  * Lecture 10: Principal Component Analysis [Notebook]

hyper-parameters

  * Lecture 19: Random Forrest II
  * Lecture 19: Random Forrest II [Notebook]
  * Lecture 18: Random Forest
  * Lecture 18: Random Forest [Notebook]
  * Lecture 18: Random Forest [Notebook]
  * Lecture 18: Random Forest [Notebook]

Imbalanced classes

  * Lecture 19: Random Forrest II
  * Lecture 19: Random Forrest II [Notebook]

Imputation

  * Lecture 9: Missing Data & Imputation
  * Lecture 9: Missing Data & Imputation [Notebook]
  * Lecture 9: Missing Data & Imputation [Notebook]

Imputation with uncertainty

  * Lecture 9: Missing Data & Imputation
  * Lecture 9: Missing Data & Imputation [Notebook]
  * Lecture 9: Missing Data & Imputation [Notebook]

Independence

  * Lecture 8: Inference in Regression and Hypothesis Testing
  * Lecture 8: Inference in Regression and Hypothesis Testing 
    [Notebook]
  * Lecture 8: Inference in Regression and Hypothesis Testing 
    [Notebook]
  * Lecture 8: Inference in Regression and Hypothesis Testing 
    [Notebook]
  * Lecture 8: Inference in Regression and Hypothesis Testing 
    [Notebook]
  * Lecture 8: Inference in Regression and Hypothesis Testing 
    [Notebook]

Instructor

  * Lecture 1: Introduction to CS109A

interaction effect

  * Lecture 4: Multi-linear and Polynomial Regression
  * Lecture 4: Multi-linear and Polynomial Regression [Notebook]
  * Lecture 4: Multi-linear and Polynomial Regression [Notebook]
  * Lecture 4: Multi-linear and Polynomial Regression [Notebook]

Interaction Terms

  * Lecture 10: Principal Component Analysis
  * Lecture 10: Principal Component Analysis [Notebook]
  * Lecture 10: Principal Component Analysis [Notebook]
  * Lecture 10: Principal Component Analysis [Notebook]

Intro

  * Lecture 1: Introduction to CS109A
  * Lecture 1: Introduction to CS109A [Notebook]

K-Fold

  * Lecture 5: Model Selection and Cross Validation
  * Lecture 5: Model Selection and Cross Validation [Notebook]
  * Lecture 5: Model Selection and Cross Validation [Notebook]

Knn

  * Lecture 3: Introduction to Regression kNN and Linear Regression
  * Lecture 3: Introduction to Regression kNN and Linear Regression 
    [Notebook]
  * Lecture 3: Introduction to Regression kNN and Linear Regression 
    [Notebook]
  * Lecture 3: Introduction to Regression kNN and Linear Regression 
    [Notebook]
  * Lecture 3: Introduction to Regression kNN and Linear Regression 
    [Notebook]
  * Lecture 3: Introduction to Regression kNN and Linear Regression 
    [Notebook]

Knn Regression

  * Lecture 3: Introduction to Regression kNN and Linear Regression
  * Lecture 3: Introduction to Regression kNN and Linear Regression 
    [Notebook]
  * Lecture 3: Introduction to Regression kNN and Linear Regression 
    [Notebook]
  * Lecture 3: Introduction to Regression kNN and Linear Regression 
    [Notebook]
  * Lecture 3: Introduction to Regression kNN and Linear Regression 
    [Notebook]
  * Lecture 3: Introduction to Regression kNN and Linear Regression 
    [Notebook]

Lasso

  * Lecture 6: Regularization Ridge and Lasso Regression
  * Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
  * Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
  * Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
  * Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
  * Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
  * S-Section 04: Regularization and Model Selection

Learning Rate

  * Lecture 20: Boosting, Gradient Boosting
  * Lecture 20: Boosting, Gradient Boosting [Notebook]

Leave-One-Out

  * Lecture 5: Model Selection and Cross Validation
  * Lecture 5: Model Selection and Cross Validation [Notebook]
  * Lecture 5: Model Selection and Cross Validation [Notebook]

Likelihood

  * Lecture 15: Logistic Regression II
  * Lecture 15: Logistic Regression II [Notebook]
  * Lecture 15: Logistic Regression II [Notebook]
  * Lecture 14: Logistic Regression I
  * Lecture 14: Logistic Regression I [Notebook]
  * Lecture 7: Probability
  * Lecture 7: Probability [Notebook]
  * Lecture 7: Probability [Notebook]

Linearity

  * Lecture 8: Inference in Regression and Hypothesis Testing
  * Lecture 8: Inference in Regression and Hypothesis Testing 
    [Notebook]
  * Lecture 8: Inference in Regression and Hypothesis Testing 
    [Notebook]
  * Lecture 8: Inference in Regression and Hypothesis Testing 
    [Notebook]
  * Lecture 8: Inference in Regression and Hypothesis Testing 
    [Notebook]
  * Lecture 8: Inference in Regression and Hypothesis Testing 
    [Notebook]

Logistic Estimation

  * Lecture 15: Logistic Regression II
  * Lecture 15: Logistic Regression II [Notebook]
  * Lecture 15: Logistic Regression II [Notebook]
  * Lecture 14: Logistic Regression I
  * Lecture 14: Logistic Regression I [Notebook]

Logistic Regression

  * Lecture 15: Logistic Regression II
  * Lecture 15: Logistic Regression II [Notebook]
  * Lecture 15: Logistic Regression II [Notebook]
  * Lecture 14: Logistic Regression I
  * Lecture 14: Logistic Regression I [Notebook]
  * Lab 06: Principal Components Analysis (PCA)

Loss function

  * Lecture 15: Logistic Regression II
  * Lecture 15: Logistic Regression II [Notebook]
  * Lecture 15: Logistic Regression II [Notebook]

Matplotlib

  * Lecture 12: Visualization
  * Lecture 12: Visualization [Notebook]

MDI

  * Lecture 19: Random Forrest II
  * Lecture 19: Random Forrest II [Notebook]
  * Lecture 18: Random Forest
  * Lecture 18: Random Forest [Notebook]
  * Lecture 18: Random Forest [Notebook]
  * Lecture 18: Random Forest [Notebook]

Missing at Random (MAR)

  * Lecture 9: Missing Data & Imputation
  * Lecture 9: Missing Data & Imputation [Notebook]
  * Lecture 9: Missing Data & Imputation [Notebook]

Missing Completely at Random (MCAR)

  * Lecture 9: Missing Data & Imputation
  * Lecture 9: Missing Data & Imputation [Notebook]
  * Lecture 9: Missing Data & Imputation [Notebook]

Missing Data

  * Lecture 9: Missing Data & Imputation
  * Lecture 9: Missing Data & Imputation [Notebook]
  * Lecture 9: Missing Data & Imputation [Notebook]

Missing Not at Random (MNAR)

  * Lecture 9: Missing Data & Imputation
  * Lecture 9: Missing Data & Imputation [Notebook]
  * Lecture 9: Missing Data & Imputation [Notebook]

MNIST

  * Lab 06: Principal Components Analysis (PCA)

Model Interpretation

  * Lecture 4: Multi-linear and Polynomial Regression
  * Lecture 4: Multi-linear and Polynomial Regression [Notebook]
  * Lecture 4: Multi-linear and Polynomial Regression [Notebook]
  * Lecture 4: Multi-linear and Polynomial Regression [Notebook]

model selection

  * S-Section 04: Regularization and Model Selection

MSE

  * Lecture 5: Model Selection and Cross Validation
  * Lecture 5: Model Selection and Cross Validation [Notebook]
  * Lecture 5: Model Selection and Cross Validation [Notebook]
  * Lecture 4: Multi-linear and Polynomial Regression
  * Lecture 4: Multi-linear and Polynomial Regression [Notebook]
  * Lecture 4: Multi-linear and Polynomial Regression [Notebook]
  * Lecture 4: Multi-linear and Polynomial Regression [Notebook]
  * Lecture 3: Introduction to Regression kNN and Linear Regression
  * Lecture 3: Introduction to Regression kNN and Linear Regression 
    [Notebook]
  * Lecture 3: Introduction to Regression kNN and Linear Regression 
    [Notebook]
  * Lecture 3: Introduction to Regression kNN and Linear Regression 
    [Notebook]
  * Lecture 3: Introduction to Regression kNN and Linear Regression 
    [Notebook]
  * Lecture 3: Introduction to Regression kNN and Linear Regression 
    [Notebook]

Multi-Linear Regression

  * Lecture 4: Multi-linear and Polynomial Regression
  * Lecture 4: Multi-linear and Polynomial Regression [Notebook]
  * Lecture 4: Multi-linear and Polynomial Regression [Notebook]
  * Lecture 4: Multi-linear and Polynomial Regression [Notebook]

Nan

  * Lecture 9: Missing Data & Imputation
  * Lecture 9: Missing Data & Imputation [Notebook]
  * Lecture 9: Missing Data & Imputation [Notebook]

Normal

  * Lecture 7: Probability
  * Lecture 7: Probability [Notebook]
  * Lecture 7: Probability [Notebook]

Normality

  * Lecture 8: Inference in Regression and Hypothesis Testing
  * Lecture 8: Inference in Regression and Hypothesis Testing 
    [Notebook]
  * Lecture 8: Inference in Regression and Hypothesis Testing 
    [Notebook]
  * Lecture 8: Inference in Regression and Hypothesis Testing 
    [Notebook]
  * Lecture 8: Inference in Regression and Hypothesis Testing 
    [Notebook]
  * Lecture 8: Inference in Regression and Hypothesis Testing 
    [Notebook]

normalization

  * S-Section 04: Regularization and Model Selection

NPL

  * Lecture 23: Natural Language Processing
  * Lecture 23: Natural Language Processing [Notebook]
  * Lecture 23: Natural Language Processing [Notebook]

One hot encoding

  * Lecture 4: Multi-linear and Polynomial Regression
  * Lecture 4: Multi-linear and Polynomial Regression [Notebook]
  * Lecture 4: Multi-linear and Polynomial Regression [Notebook]
  * Lecture 4: Multi-linear and Polynomial Regression [Notebook]

Out of Bag Error (OOB)

  * Lecture 18: Random Forest
  * Lecture 18: Random Forest [Notebook]
  * Lecture 18: Random Forest [Notebook]
  * Lecture 18: Random Forest [Notebook]

Overfitting

  * Lecture 18: Random Forest
  * Lecture 18: Random Forest [Notebook]
  * Lecture 18: Random Forest [Notebook]
  * Lecture 18: Random Forest [Notebook]
  * Lecture 17: Bagging
  * Lecture 17: Bagging [Notebook]
  * Lecture 17: Bagging [Notebook]
  * Lecture 17: Bagging [Notebook]
  * Lecture 5: Model Selection and Cross Validation
  * Lecture 5: Model Selection and Cross Validation [Notebook]
  * Lecture 5: Model Selection and Cross Validation [Notebook]

P-Value

  * Lecture 8: Inference in Regression and Hypothesis Testing
  * Lecture 8: Inference in Regression and Hypothesis Testing 
    [Notebook]
  * Lecture 8: Inference in Regression and Hypothesis Testing 
    [Notebook]
  * Lecture 8: Inference in Regression and Hypothesis Testing 
    [Notebook]
  * Lecture 8: Inference in Regression and Hypothesis Testing 
    [Notebook]
  * Lecture 8: Inference in Regression and Hypothesis Testing 
    [Notebook]

pandas

  * Lab 4 [Notebook]
  * Lab 4 [Notebook]
  * Lab 3 [Notebook]
  * Lab 2 [Notebook]
  * Lab 2 [Notebook]
  * Lab 2 [Notebook]
  * Lecture 2: Introduction to PANDAS and EDA
  * Lecture 2: Introduction to Data Science [Notebook]
  * Lecture 2: Introduction to PANDAS 2 [Notebook]
  * Lecture 2: Introduction to PANDAS [Notebook]
  * Lab 1 [Notebook]

PDF/PMF

  * Lecture 7: Probability
  * Lecture 7: Probability [Notebook]
  * Lecture 7: Probability [Notebook]

PMF

  * Lecture 15: Logistic Regression II
  * Lecture 15: Logistic Regression II [Notebook]
  * Lecture 15: Logistic Regression II [Notebook]
  * Lecture 14: Logistic Regression I
  * Lecture 14: Logistic Regression I [Notebook]

Polynomial Regression

  * Lecture 4: Multi-linear and Polynomial Regression
  * Lecture 4: Multi-linear and Polynomial Regression [Notebook]
  * Lecture 4: Multi-linear and Polynomial Regression [Notebook]
  * Lecture 4: Multi-linear and Polynomial Regression [Notebook]

Prediction Intervals

  * Lecture 8: Inference in Regression and Hypothesis Testing
  * Lecture 8: Inference in Regression and Hypothesis Testing 
    [Notebook]
  * Lecture 8: Inference in Regression and Hypothesis Testing 
    [Notebook]
  * Lecture 8: Inference in Regression and Hypothesis Testing 
    [Notebook]
  * Lecture 8: Inference in Regression and Hypothesis Testing 
    [Notebook]
  * Lecture 8: Inference in Regression and Hypothesis Testing 
    [Notebook]

principal components analysis

  * Lab 06: Principal Components Analysis (PCA)

Principal Components Analysis (PCA)

  * Lecture 10: Principal Component Analysis
  * Lecture 10: Principal Component Analysis [Notebook]
  * Lecture 10: Principal Component Analysis [Notebook]
  * Lecture 10: Principal Component Analysis [Notebook]

Probability

  * Lecture 15: Logistic Regression II
  * Lecture 15: Logistic Regression II [Notebook]
  * Lecture 15: Logistic Regression II [Notebook]
  * Lecture 14: Logistic Regression I
  * Lecture 14: Logistic Regression I [Notebook]

Pruning

  * Lecture 17: Bagging
  * Lecture 17: Bagging [Notebook]
  * Lecture 17: Bagging [Notebook]
  * Lecture 17: Bagging [Notebook]

Random Forest

  * Lecture 19: Random Forrest II
  * Lecture 19: Random Forrest II [Notebook]
  * Lecture 18: Random Forest
  * Lecture 18: Random Forest [Notebook]
  * Lecture 18: Random Forest [Notebook]
  * Lecture 18: Random Forest [Notebook]

Random Variable

  * Lecture 7: Probability
  * Lecture 7: Probability [Notebook]
  * Lecture 7: Probability [Notebook]

Regression Trees

  * Lecture 17: Bagging
  * Lecture 17: Bagging [Notebook]
  * Lecture 17: Bagging [Notebook]
  * Lecture 17: Bagging [Notebook]

Regularization

  * Lecture 6: Regularization Ridge and Lasso Regression
  * Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
  * Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
  * Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
  * Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
  * Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
  * S-Section 04: Regularization and Model Selection

Residuals

  * Lecture 20: Boosting, Gradient Boosting
  * Lecture 20: Boosting, Gradient Boosting [Notebook]

Review

  * Lecture 24: Review

Ridge

  * Lecture 6: Regularization Ridge and Lasso Regression
  * Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
  * Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
  * Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
  * Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
  * Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
  * S-Section 04: Regularization and Model Selection

Standard Errors

  * Lecture 8: Inference in Regression and Hypothesis Testing
  * Lecture 8: Inference in Regression and Hypothesis Testing 
    [Notebook]
  * Lecture 8: Inference in Regression and Hypothesis Testing 
    [Notebook]
  * Lecture 8: Inference in Regression and Hypothesis Testing 
    [Notebook]
  * Lecture 8: Inference in Regression and Hypothesis Testing 
    [Notebook]
  * Lecture 8: Inference in Regression and Hypothesis Testing 
    [Notebook]

standarization

  * S-Section 04: Regularization and Model Selection

Stopping Conditions

  * Lecture 17: Bagging
  * Lecture 17: Bagging [Notebook]
  * Lecture 17: Bagging [Notebook]
  * Lecture 17: Bagging [Notebook]

Stopping Conditions & Prunin

  * Lecture 16: Decision Trees
  * Lecture 16: Decision Trees [Notebook]

synergy effect

  * Lecture 4: Multi-linear and Polynomial Regression
  * Lecture 4: Multi-linear and Polynomial Regression [Notebook]
  * Lecture 4: Multi-linear and Polynomial Regression [Notebook]
  * Lecture 4: Multi-linear and Polynomial Regression [Notebook]

T-Test

  * Lecture 8: Inference in Regression and Hypothesis Testing
  * Lecture 8: Inference in Regression and Hypothesis Testing 
    [Notebook]
  * Lecture 8: Inference in Regression and Hypothesis Testing 
    [Notebook]
  * Lecture 8: Inference in Regression and Hypothesis Testing 
    [Notebook]
  * Lecture 8: Inference in Regression and Hypothesis Testing 
    [Notebook]
  * Lecture 8: Inference in Regression and Hypothesis Testing 
    [Notebook]

Teaching Fellows

  * Lecture 1: Introduction to CS109A

Train Validation Test

  * Lecture 5: Model Selection and Cross Validation
  * Lecture 5: Model Selection and Cross Validation [Notebook]
  * Lecture 5: Model Selection and Cross Validation [Notebook]

Underfitting

  * Lecture 18: Random Forest
  * Lecture 18: Random Forest [Notebook]
  * Lecture 18: Random Forest [Notebook]
  * Lecture 18: Random Forest [Notebook]
  * Lecture 5: Model Selection and Cross Validation
  * Lecture 5: Model Selection and Cross Validation [Notebook]
  * Lecture 5: Model Selection and Cross Validation [Notebook]

Uniform

  * Lecture 7: Probability
  * Lecture 7: Probability [Notebook]
  * Lecture 7: Probability [Notebook]

Variable Importance

  * Lecture 18: Random Forest
  * Lecture 18: Random Forest [Notebook]
  * Lecture 18: Random Forest [Notebook]
  * Lecture 18: Random Forest [Notebook]

Variance

  * Lecture 6: Regularization Ridge and Lasso Regression
  * Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
  * Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
  * Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
  * Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
  * Lecture 6: Regularization Ridge and Lasso Regression [Notebook]

Variance vs Bias

  * Lecture 16: Decision Trees
  * Lecture 16: Decision Trees [Notebook]

weights

  * Lecture 21: AdaBoost
  * Lecture 21: AdaBoost [Notebook]

Copyright 2018 (c) Institute for Applied Computational Science