Introduction to Machine Learning - Class 1

Overview

Explore the foundational aspects of machine learning with renowned instructor Ralf Eichhorn. In this premiere class, delve into the essential principles and key concepts that underpin the fascinating field of machine learning.

Hosted by the School on Biological Physics and Biomolecular Simulations, this session is perfect for beginners eager to understand the basics and broaden their knowledge. Available on YouTube, this course is categorized under Artificial Intelligence and Computer Science, providing valuable insights for aspiring learners.

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

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Subjects

Computer Science