STEM Program
Fundamental AI and Machine Learning
An AI Engineer Training Program with Live Python Tutorials
with Harvard Professor Dr. Xing · 96 Instruction Hours
From zero experience to a working ML engineer's toolkit.
This program is an intensive, hands-on introduction to applied machine learning and AI engineering, designed for beginners. It emphasizes practical skills, intuitive understanding, and real-world problem solving.
By the end, students are capable of performing the core tasks expected of a junior AI or machine learning engineer, including data analysis, model development, evaluation, and basic optimization.
Dr. Fangxu Xing
Dr. Fangxu Xing is a Harvard professor with over 15 years of experience in artificial intelligence, machine learning, and computer vision. He earned his degree from Johns Hopkins University, trained as a postdoctoral researcher and AI engineer at Massachusetts General Hospital, and has since authored hundreds of peer-reviewed publications on AI applications in healthcare and beyond.
Over 11 years at Harvard, he has hands-on trained more than 800 students and learners, many of whom have gone on to major positions in industry, including FAANG+ companies.
Passionate about AI education, Dr. Xing makes machine learning approachable, practical, and engaging, helping students connect core theory to real-world impact.
- Free-mask: A novel paradigm of integration between the segmentation diffusion model and image editing
- Label Space-Induced Pseudo Label Refinement for Multi-Source Black-Box Domain Adaptation
- Deep unsupervised domain adaptation: A review of recent advances and perspectives
- Adversarial unsupervised domain adaptation with conditional and label shift: Infer, align and iterate
- Subtype-aware unsupervised domain adaptation for medical diagnosis
Every session runs on the same loop.
Roughly three hours, split into two to three algorithm modules. Each module moves through the same four stages before a short break and the next module begins.
Twelve weeks, five stages.
Combining lectures, live coding, case studies, and hands-on projects, building from Python fundamentals to a full capstone project.
- Introduction to AI and machine learning
- Python programming for AI
- Data structures, scientific libraries (NumPy, Pandas, Matplotlib)
- First classification and regression models (KNN, Naive Bayes, Linear & Logistic Regression)
- Decision Trees and Support Vector Machines
- Bias–variance tradeoff
- Ensemble methods: bagging, boosting, stacking
- Introductory neural networks
- Intuitive calculus for machine learning
- Gradient descent and optimization algorithms
- Statistics for ML: correlation, Gaussian models, Central Limit Theorem
- Exploratory data analysis and data cleaning
- Feature engineering techniques
- Dimensionality reduction (PCA, t-SNE)
- Clustering methods (K-Means, Hierarchical, DBSCAN)
- Model evaluation metrics (accuracy, precision, recall, F1, ROC/AUC)
- Regularization and hyperparameter tuning
- Capstone project using a real-world dataset
- Weekly coding exercises
- A final end-to-end AI project
- A written technical report or paper
- A formal program evaluation
- A potential letter of recommendation from a Harvard professor
- Build and evaluate machine learning models independently
- Understand how AI systems work in real-world applications
- Read and modify existing ML codebases
- Complete an end-to-end AI project using real industry datasets
- Build a strong foundation for advanced study in deep learning, computer vision, NLP, and AI research
- Prepare for internships and entry-level roles: Junior AI Engineer, Junior ML Engineer, ML-focused Data Analyst, AI Research Assistant
High schoolers and undergrads, starting from zero.
This program is open to high school and undergraduate students interested in AI engineering, data science, or applied AI careers, who have no prior experience.
Prerequisite: basic algebra onlyReady to start building?
Space is limited to 20–30 students per cohort. Apply now to secure your seat this fall.
Apply Now