Welcome to the teaching hub. I use this space to share additional materials, readings and interactive tools with students in my courses. My philosophy is rooted in intelligence augmentation and hybrid human ‑ AI learning. Rather than replacing educators, artificial intelligence can scaffold our development and act as a cognitive partner. Structured prompting and guided dialogue with AI agents encourages deeper thinking, activates metacognition and introduces constructive challenges for learners. By combining the capabilities of machines with human judgement we can transform our classrooms into spaces where each student receives personalised support. Please explore the links and resources here, and use them in conjunction with our LMS.
50.038 Computational Data Science (Jan 2026)
Overview & Philosophy This course offers a practical approach to data science, focusing on how models work, when to apply specific techniques, and how to evaluate performance. While foundational math is used to explain mechanics, the curriculum avoids heavy mathematical proofs in favor of hands-on application. The goal is to equip students to tackle complete data science projects; from data gathering and pre-processing to analysis using machine learning tools.
Curriculum Trajectory The syllabus moves hierarchically from foundational big data handling to advanced autonomous AI:
- Foundations (Weeks 1–5): The course begins with Big Data architectures like Hadoop and MapReduce, followed by essential data science techniques including data visualization, feature vectors, dimension reduction (PCA), regression, time series, and classification algorithms (SVM, Decision Trees, k-NN).
- Deep Learning & NLP (Weeks 6–11): The focus shifts to neural networks, covering Multilayer Perceptrons (MLP), Natural Language Processing (Word2Vec, Transformers), Convolutional Neural Networks (CNNs), and memory models like RNNs and LSTMs.
- Advanced Frontier (Week 12): The curriculum concludes with emerging topics in Agentic Data Science.
Machine Learning Concepts Made Easy
The Mathematical Toolkit for Data Science
