Machine Learning Applications and Practices
This tutorial was designed and taught in Summer 2024, attended by both graduate and undergraduate students. It introduced core machine learning concepts and emphasized hands-on experience with real-world workflows, including data preprocessing, model selection, and application development.
The course also covered fine-tuning large language models (LLMs) for specific downstream tasks and building LLM-based applications, such as ChatPDF, using tools like BERT and LangChain. Students used Python-based frameworks to implement practical solutions and gain a deeper understanding of applied machine learning and generative AI techniques.
📅 Weekly Schedule
Week 1
- May 31 – Lecture 1: Course Overview and Introduction to Machine Learning. [Slides]
- June 2 – Lab 1: Installing Python (File 1), Running Simple ML Examples (File 2).
Week 2
- Watching video clips: The Sun as the Primary Weather Forcing Factor.
- June 7 – Lecture 2: Feature Selection and ML Classification. [Slides]
- June 8 – Q&A.
- June 9 – Lab 2: Labeling Twitter Data, Feature Extraction. Data Label Sample Codes, Feature Extraction Sample Codes
Week 3
- Watching video clips: What happens when the sun hits the Earth’s Surface?
- June 14 – Lecture 3: Neural Networks and Deep Learning Fundamentals. [Slides]
- June 15 – Q&A.
- June 16 – Lab 3: Coding for ML Algorithms. ML Classification Sample Codes
Week 4
- Watching video clips: Where weather affects us: The Boundary Layer?
- June 21 – Lecture 4: Theory of Deep Learning. Slides
- June 22 – Q&A
- June 23 – Lab 4: Coding for LSTM and CNN. Sample Codes
Week 5
- Watching video clips: Forecasting Basics?
- June 28 – Lecture 5: Weather Forecasting: Introduction to Mesonet and WRF-HRRR Data, and Forecasting Modelets. Slides
- June 29 – Q&A.
- June 30 – Lab 5: Downloading Data of Interests and Extracting Features. Download Sample Data, and Micro Sample Codes and Micro Running Modelets Mesonet and WRF Sample Codes.
Week 6
- Watching video clips: Severe Weather.
- July 5 – Lecture 6: Reinforcement Learning. Slides
- July 6 – Q&A.
- July 7 – Lab 6: Coding for Reinforcement Learning. Sample Codes
Week 7
- Watching video clips: Hurricanes.
- July 12 – Lecture 7: Introduction of Large Language Models (LLMs). Slides
- July 13 – Q&A.
- July 14 – Lab 7: Hands-on Experience with Training and Using LLMs. Lab
Week 8
- Watching video clips: Measuring the Weather with Instruments and Weather Stations.
- July 19 – Lecture 8: How Does ChatGPT work? Slides
- July 20 – Q&A.
- July 21 – Lab 8: Hands-on Experience with Training and Using LLMs. Lab
Week 9
- Watching video clips: Observing Weather with Radar.
- July 26 – Lecture 9: LLMs for Real-world Applications. Slides
- July 27 – Q&A.
- July 28 – Lab 9: Reporting.