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episodes/03-ml-1.md

@@ -37,14 +37,14 @@ Before diving into ML in HEP, participants should have a basic understanding of:
 
 ### Detailed Concepts and Steps
 
-## 1. Data Acquisition and Understanding
+## Data Acquisition and Understanding
 
 ### CMS Open Data Overview
 - Accessing and understanding the CMS Open Data repository.
 - Types of datasets available (e.g., AOD, MiniAOD, NanoAOD) and their differences.
 - Introduction to the CMS experiment and its detectors.
 
-## 2. Data Preparation
+## Data Preparation
 
 ### Cleaning and Preprocessing
 - Handling missing data points and outliers.
@@ -56,7 +56,7 @@ Before diving into ML in HEP, participants should have a basic understanding of:
 - Creating new features to enhance model performance.
 - Dimensionality reduction techniques (PCA, t-SNE) for visualization and model efficiency.
 
-## 3. Supervised Learning in HEP
+## Supervised Learning in HEP
 
 ### Basics of Supervised Learning
 - Understanding labeled datasets and target variables.
@@ -73,7 +73,7 @@ Before diving into ML in HEP, participants should have a basic understanding of:
 - Confusion matrices and ROC curves for performance visualization.
 - Interpreting results and refining models based on feedback.
 
-## 4. Unsupervised Learning in HEP
+## Unsupervised Learning in HEP
 
 ### Basics of Unsupervised Learning
 - Clustering algorithms (K-means, DBSCAN) for grouping similar events.
@@ -85,7 +85,7 @@ Before diving into ML in HEP, participants should have a basic understanding of:
 - Grouping events based on similar characteristics (clustering).
 - Simplifying complex datasets for further analysis.
 
-## 5. Advanced Topics and Applications
+## Advanced Topics and Applications
 
 ### Deep Learning and Neural Networks
 - Introduction to deep learning architectures (CNNs, RNNs).