Dr Alina Donea, Monash University
From Equations to Intelligence: A Deep Dive into Data Science and AI is an intensive course designed to bridge mathematical foundations with practical applications in modern Artificial Intelligence. Spanning four weeks, it provides participants with a rigorous yet accessible pathway from the fundamentals of data science to the core mechanics of deep learning.
Week 1 introduces the landscape of Data Science, covering its impact, career opportunities, and essential tools such as data structures, representations, and visualization, with a focus on Python (intermediate level).
Week 2 consolidates the mathematical backbone of AI, revisiting advanced linear algebra, probability, and statistics, and introducing core techniques such as Singular Value Decomposition, PCA, and optimization algorithms. Neural networks are introduced alongside Python problem sets to strengthen applied understanding.
Week 3 dives deeper into the mathematics of machine learning models, explaining the principles behind convolutional neural networks, support vector machines, sequence models, and generative models. Practical coding sessions ensure theoretical concepts are tied to implementation.
Week 4 focuses on advanced aspects of deep learning, including feature selection, convex optimization, runtime considerations, and modern architectures such as Transformers.
Week 1: Data Science
Week 2: Mathematics for AI
Week 3: Breaking Down the Mathematics Behind ML Models 1: A Comprehensive Guide and Applications
Week 4: Mathematics of Deep Learning 2: A Comprehensive Guide and Applications
• First- and second-year linear algebra (linear maps, kernel, image, etc)
• Probabilities second year, Calculus second year level.
• Basic coding experience is required. Pre-workshop materials will be provided, and completing the beginner Python module (on Moodle) before the school starts is compulsory.
1. Assignments – 45%
2. GitHub, Coding Beauty, Data Visualisation – 5%
3. Team Leadership , Use of AI Tools – 15%
4. Final Project – 35% (Test online 5%, Project 30%)
Students are expected to bring their own laptops/devices to Summer School to complete this subject.
Take this pre-enrolment QUIZ to self-evaluate and get a measure of the key foundational knowledge required. NOTE: this is a google drive link containing a Jupyter python notebook.