UC San Diego: Machine Learning Fundamentals Course Syllabus

Overview: This course provides a structured introduction to machine learning fundamentals, designed for learners beginning their journey in AI and data science. Over approximately 15-20 hours, participants will explore core concepts through hands-on labs, real-world case studies, and guided projects. The curriculum emphasizes practical skills in data preprocessing, statistical analysis, model development, and visualization, using industry-standard tools. With clear explanations and instructor feedback, the course builds a strong foundation for further study or entry into data-driven roles.

Module 1: Data Exploration & Preprocessing

Estimated time: 3 hours

• Exploratory data analysis workflows
• Data cleaning and transformation techniques
• Feature engineering basics
• Best practices in data preprocessing

Module 2: Statistical Analysis & Probability

Estimated time: 4 hours

• Foundations of statistical analysis
• Probability theory and distributions
• Applying statistics to extract insights from data
• Tools and frameworks for statistical computing

Module 3: Machine Learning Fundamentals

Estimated time: 2 hours

• Introduction to machine learning concepts
• Supervised vs. unsupervised learning
• Model training and prediction workflow

Module 4: Model Evaluation & Optimization

Estimated time: 2 hours

• Evaluation metrics for ML models
• Overfitting and underfitting detection
• Hyperparameter tuning basics

Module 5: Data Visualization & Storytelling

Estimated time: 3 hours

• Principles of effective data visualization
• Creating communicative charts and dashboards
• Data storytelling techniques for impact

Module 6: Advanced Analytics & Feature Engineering

Estimated time: 4 hours

• Advanced feature engineering methods
• Working with large-scale datasets
• Applying analytics to real-world problems
• Guided project with instructor feedback

Prerequisites

• Basic understanding of Python programming
• Familiarity with fundamental mathematics and statistics
• Access to a computer with internet for labs and tools

What You'll Be Able to Do After

• Implement data preprocessing and feature engineering techniques
• Apply statistical methods to extract insights from complex data
• Build and evaluate machine learning models using real-world datasets
• Create data visualizations that communicate findings effectively
• Work with large-scale datasets using industry-standard tools