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The last decade has witnessed the rise of Artificial Intelligence and Machine Learning (AI/ML), which has not only driven technological progress in a variety of sectors, but has also entered our daily lives. This recent growth spurt of AI/ML owes in large part to the emergence of large data sets, along with cheap sensing and computing hardware. Yet, the remarkable successes of the field must also be attributed to its strong mathematical foundations, which have been developed for over half a century. To obtain a deep understanding of AI/ML technology, it is essential to become familiar with its mathematical underpinnings.
This course will discuss a variety of topics in AI/ML, in each case presenting a mathematical model and accompanying solutions. Tasks considered will include supervised learning with linear and non-linear models, unsupervised learning, on-line learning, search, and knowledge representation. In turn, these tasks will be modeled and solved using building blocks such as vectors, differential calculus, probability, optimisation, algorithms and proofs, and linear algebra.
The course is meant for students, teachers, and professionals who are interested in the conceptual foundations of AI/ML. Applicants must have an undergraduate degree in science, mathematics, or engineering. The laboratory will require programming in Python (a few tens of lines of code per exercise). Registrants must either have experience in programming or be willing to learn it independently (some basic tutorials will be provided).
Note: Participants must bring their own laptops, which can connect to the Internet through WiFi. No special software is required to be installed.
The course will be held February 20–24 (Monday–Friday), with the following daily schedule.
| 9.30 a.m.–11.00 a.m. | Lecture 1 |
| 11.00 a.m.–11.20 a.m. | Break for tea |
| 11.20 a.m.–12.50 p.m. | Lecture 2 |
| 12.50 p.m.–1.50 p.m. | Lunch |
| 1.50 p.m.–3.50 p.m. | Lab |
| 3.50 p.m.–4.10 p.m. | Break for tea |
| 4.10 p.m.–5.10 p.m. | Lecture 3 |