diff --git a/slides/regularization/rsrc/cv-error-lambda-path.py b/slides/regularization/rsrc/cv-error-lambda-path.py
deleted file mode 100644
index 7d2c3738..00000000
--- a/slides/regularization/rsrc/cv-error-lambda-path.py
+++ /dev/null
@@ -1,52 +0,0 @@
-from sklearn.datasets import load_wine
-from sklearn.model_selection import train_test_split
-from sklearn.preprocessing import StandardScaler
-from sklearn.linear_model import RidgeCV
-import numpy as np
-import matplotlib.pyplot as plt
-
-# Load wine dataset
-X, y = load_wine(return_X_y=True)
-
-# Generating standard normal noise features
-np.random.seed(42)
-noise_features = np.random.normal(size=(X.shape[0], 75))
-
-# Adding these noise features to the original dataset
-X_extended = np.hstack((X, noise_features))
-
-# Splitting the augmented dataset into training and test sets
-X_train_ext, X_test_ext, y_train_ext, y_test_ext = train_test_split(
-    X_extended, y, test_size=0.2, random_state=42
-)
-
-# Standardizing the augmented dataset
-scaler_ext = StandardScaler()
-X_train_ext_scaled = scaler_ext.fit_transform(X_train_ext)
-X_test_ext_scaled = scaler_ext.transform(X_test_ext)
-
-# Define a range of lambda (alpha) values
-lambda_values = np.logspace(-4, 4, 50)
-
-# Performing Ridge Regression with Cross-Validation on the extended dataset
-ridge_cv_ext = RidgeCV(alphas=lambda_values, store_cv_values=True)
-ridge_cv_ext.fit(X_train_ext_scaled, y_train_ext)
-
-# Plotting the CV Curve for the extended dataset
-mean_cv_scores_ext = np.mean(ridge_cv_ext.cv_values_, axis=0)
-
-# Finding the lambda value with the minimum CV score
-min_lambda_index = np.argmin(mean_cv_scores_ext)
-min_lambda_value = lambda_values[min_lambda_index]
-
-# Re-plotting with a vertical blue bar at the minimum CV score
-plt.figure(figsize=(8, 6))
-plt.plot(lambda_values, mean_cv_scores_ext, marker='o', color='red')
-plt.axvline(x=min_lambda_value, color='blue', linestyle='--', label=f'Min CV Score at λ={min_lambda_value:.4f}')
-plt.xscale('log')
-plt.xlabel('Lambda (Regularization strength)', fontsize = 14)
-plt.ylabel('Generalization error', fontsize = 14)
-#plt.title('Wine dataset with add. noise features', fontsize=12)
-plt.title('Effect of L2 Regularization', fontsize = 16)
-plt.legend()
-plt.show()
diff --git a/slides/regularization/slides-regu-ridge-deepdive.tex b/slides/regularization/slides-regu-ridge-deepdive.tex
index 1d684cfd..199e82f8 100644
--- a/slides/regularization/slides-regu-ridge-deepdive.tex
+++ b/slides/regularization/slides-regu-ridge-deepdive.tex
@@ -3,7 +3,7 @@
 \input{../../latex-math/basic-math}
 \input{../../latex-math/basic-ml}
 
-\newcommand{\titlefigure}{figure_man/cv-error-lambda-path.png}
+\newcommand{\titlefigure}{figure_man/bias-variance-ridge.png}
 \newcommand{\learninggoals}{
   \item Know interpretation of $L2$ regularization as row-augmentation
   \item Know interpretation of $L2$ regularization as minimizing risk under feature noise