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AdaBoost

• Lecture 21: AdaBoost
• Lecture 21: AdaBoost [Notebook]

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• 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]