From d25f718260c99ca42befe5b0a26a89c50f8301bc Mon Sep 17 00:00:00 2001 From: alextintin007 Date: Tue, 16 Jul 2024 15:23:22 -0500 Subject: [PATCH] Update Content --- episodes/03-ml-1.md | 88 ++++++++++++++++++++++++++++++++++----------- episodes/04-ml-2.md | 2 +- 2 files changed, 69 insertions(+), 21 deletions(-) diff --git a/episodes/03-ml-1.md b/episodes/03-ml-1.md index a60bed6..bd48f08 100644 --- a/episodes/03-ml-1.md +++ b/episodes/03-ml-1.md @@ -1,5 +1,5 @@ --- -title: "Machine Learning with Open Data" +title: "Introduction to Machine Learning in HEP" teaching: 5 exercises: 0 --- @@ -22,40 +22,88 @@ exercises: 0 # Introduction to Machine Learning in HEP -This lesson guides participants through applying machine learning techniques to CMS Open Data. It's designed for those with some programming experience and an interest in machine learning. +Machine learning (ML) is a powerful tool for extracting insights from complex datasets, making it invaluable in high-energy physics (HEP) research. This lesson introduces participants to applying ML techniques to CMS Open Data, providing a structured approach for beginners with some programming experience. ### Overview -The Machine Learning in High-Energy Physics (HEP) activity provides participants with an introduction to the exciting intersection of machine learning and particle physics. By leveraging CMS Open Data, participants will learn how to apply machine learning algorithms to real-world data, enhancing their analytical skills and understanding of both fields. +The Machine Learning in High-Energy Physics (HEP) activity bridges the gap between data science and particle physics, utilizing CMS Open Data to explore real-world applications. Participants will learn to leverage ML algorithms to analyze particle collision data, enabling them to classify events, discover new particles, and enhance their understanding of fundamental physics. -Machine learning plays a crucial role in analyzing vast amounts of data generated by experiments in High-Energy Physics (HEP). It enables researchers to extract meaningful insights, classify particle collisions, and discover new physics phenomena efficiently. +### Prerequisites -# Data Preparation -A crucial step in any machine learning project is data preparation. Participants will learn how to clean and preprocess CMS Open Data to make it suitable for machine learning algorithms. This includes handling missing values, normalizing data, and creating training and test datasets. +Before diving into ML in HEP, participants should have a basic understanding of: +- Programming fundamentals (Python recommended) +- Data handling and visualization +- Elementary statistical concepts (mean, variance, etc.) -## Supervised Learning in HEP +### Detailed Concepts and Steps -- Definition: Supervised learning involves training a model on a labeled dataset, where each input data point is paired with its corresponding target label or output. -- Objective: The goal is to learn a mapping from inputs to outputs, based on the labeled examples provided during training. -- Examples: Classification (predicting categories), regression (predicting continuous values), and sequence prediction tasks are common supervised learning problems. -- Process: Models are trained using algorithms that minimize the error between predicted and actual outputs, adjusting parameters to improve accuracy. +## 1. Data Acquisition and Understanding -## Unsupervised Learning in HEP +### 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. -- Definition: Unsupervised learning involves training a model on an unlabeled dataset, where the algorithm tries to identify patterns, relationships, or structures in the data without explicit guidance. -- Objective: The goal is to explore the data and extract meaningful insights, such as clusters, associations, or anomalies. -- Examples: Clustering (grouping similar data points), anomaly detection (identifying unusual patterns), and dimensionality reduction (reducing the number of features while preserving important information) are common unsupervised learning tasks. -- Process: Algorithms in unsupervised learning rely on statistical properties of the data to uncover patterns or structures. They do not aim to predict specific outputs but rather to understand the inherent structure of the data. +## 2. Data Preparation -# Model Training and Evaluation +### Cleaning and Preprocessing +- Handling missing data points and outliers. +- Normalizing data to ensure consistency across features. +- Exploratory data analysis (EDA) to understand distributions and correlations. -Participants will gain hands-on experience in training and evaluating machine learning models. This includes selecting appropriate algorithms, tuning hyperparameters, and assessing model performance using metrics such as accuracy, precision, recall, and F1 score. +### Feature Engineering +- Selecting relevant features for ML models. +- Creating new features to enhance model performance. +- Dimensionality reduction techniques (PCA, t-SNE) for visualization and model efficiency. +## 3. Supervised Learning in HEP + +### Basics of Supervised Learning +- Understanding labeled datasets and target variables. +- Classification tasks: distinguishing particle types (e.g., muons, electrons). +- Regression tasks: predicting particle properties (e.g., energy, momentum). + +### Model Selection and Training +- Choosing appropriate algorithms (e.g., Decision Trees, Random Forests, Neural Networks). +- Cross-validation techniques to optimize model performance. +- Hyperparameter tuning to fine-tune model behavior. + +### Model Evaluation +- Metrics: accuracy, precision, recall, F1-score. +- Confusion matrices and ROC curves for performance visualization. +- Interpreting results and refining models based on feedback. + +## 4. Unsupervised Learning in HEP + +### Basics of Unsupervised Learning +- Clustering algorithms (K-means, DBSCAN) for grouping similar events. +- Anomaly detection techniques to identify unusual data points. +- Dimensionality reduction methods (PCA, LDA) for visualizing high-dimensional data. + +### Applications in Particle Physics +- Discovering new particles through anomaly detection. +- Grouping events based on similar characteristics (clustering). +- Simplifying complex datasets for further analysis. + +## 5. Advanced Topics and Applications + +### Deep Learning and Neural Networks +- Introduction to deep learning architectures (CNNs, RNNs). +- Applications in image analysis (e.g., detector images) and sequential data (e.g., event sequences). + +### Reinforcement Learning +- Exploring dynamic decision-making processes in particle physics experiments. +- Potential applications in optimizing experimental setups and data collection strategies. + +# Conclusion + +The Machine Learning with Open Data lesson equips participants with fundamental skills to apply ML techniques effectively in high-energy physics research. By mastering data preparation, model training, and evaluation, participants gain insights into both machine learning principles and their practical applications in particle physics. ::::::::::::::::::::::::::::::::::::: keypoints - Introduction to machine learning in particle physics. -- Data preparation for machine learning analysis. -- Model training and evaluation techniques. +- Comprehensive data preparation for machine learning analysis. +- Supervised and unsupervised learning techniques specific to HEP. +- Advanced ML applications in particle physics research. :::::::::::::::::::::::::::::::::::::::::::::::: diff --git a/episodes/04-ml-2.md b/episodes/04-ml-2.md index cbae458..68f0337 100644 --- a/episodes/04-ml-2.md +++ b/episodes/04-ml-2.md @@ -1,5 +1,5 @@ --- -title: "Machine Learning with Open Data" +title: "Machine Learning Practical Applications" teaching: 5 exercises: 0 ---