The mission of this 5-day course is to equip high school students who already have Python skills with foundational knowledge and hands-on experience in artificial intelligence, machine learning, and deep learning—using Python and PyTorch—to analyze scientific data, process images, and extract insights from scientific text.
The syllabus of the afternoon lecture and hands-on sessions from 1pm to 4pm is listed below.
| Day | Module Title | Key Topics | Learning Objectives | Lecture (1.5hr) | Hands-on (1.5hr) |
|---|---|---|---|---|---|
| 1 | AI Foundations, Python & Data Science Review | What is AI? Python basics, Pandas, Matplotlib | Understand AI concepts and real-world applications; Review Python syntax; Manipulate and visualize scientific data | Overview of AI in science/engineering; Python recap (variables, loops, functions); Data handling and visualization with Pandas/Matplotlib | Clean and analyze a science dataset (e.g., astronomy, physics) and plot results using Pandas and Matplotlib |
| 2 | Machine Learning & Deep Learning Basics | ML vs. DL, Linear Regression, Neural Networks, PyTorch | Distinguish ML from DL; Train a simple neural network using PyTorch | Introduction to ML algorithms and deep learning; Linear regression vs. neural networks; PyTorch basics | Build and train a simple neural network for regression/classification on a science dataset using PyTorch |
| 3 | Convolutional Neural Networks (CNNs) for Image Processing | CNNs, feature detection, image classification | Understand how CNNs extract features (edges, shapes); Build an image classifier for scientific images | CNN architecture and scientific applications (e.g., microscopy, astronomy); Feature maps and filters | Train a CNN to classify scientific images (e.g., hand-written digits, cell images) |
| 4 | Reinforcement Learning | Reinforcement Learning teaches how autonomous agents learn to make decisions through trial a | Understand the basics of reinforcement learning, including its key concepts, approaches, and real-world applications. | Autonomous systems, the RL learning loop of states, actions, and rewards, policy and value-based methods, and challenges like the alignment problem. | Build a reinforcement learning agent in Colab, |
| 5 | Natural Language Processing (NLP) in Science & Engineering | NLP fundamentals, text preprocessing, sentiment analysis | Learn how computers process language; Build a basic NLP application for scientific text | Introduction to NLP and its applications; Text preprocessing (tokenization, stop words); Sentiment analysis with Python (NLTK or spaCy) | Try Generative AI models. |
