Introduction to Machine Learning - Class 1

Overview

Dive into the fundamentals of machine learning with Ralf Eichhorn, exploring key concepts and principles in this introductory session from the School on Biological Physics and Biomolecular Simulations.

Syllabus

• Introduction to the Course
Overview of Machine Learning
Course Objectives and Outcomes
Introduction to the Instructor: Ralf Eichhorn
• Fundamentals of Machine Learning
Definition and Types of Machine Learning
Key Concepts: Features, Labels, and Models
• Overview of Machine Learning Algorithms
Supervised Learning
Unsupervised Learning
Reinforcement Learning
• Understanding Data in Machine Learning
Types of Data: Structured vs. Unstructured
Data Preprocessing Techniques
Feature Selection and Engineering
• Model Evaluation and Validation
Key Metrics: Accuracy, Precision, Recall, F1 Score
Cross-Validation Techniques
• Introduction to Biological Physics and Biomolecular Simulations
Relevance of Machine Learning in Biological Physics
Case Studies: Applications in Biomolecular Simulations
• Tools and Software
Introduction to Popular Machine Learning Libraries
Setting Up a Machine Learning Environment
• Practical Session and Demonstration
Simple Machine Learning Models: Hands-on Example
Using R or Python for Implementing Basic Models
• Discussion and Q&A
Recap of Key Concepts
Open Floor for Questions and Discussion
• Final Remarks and Next Steps
Recommended Reading and Resources
Future Learning Opportunities and Advanced Courses

Subjects

Computer Science