Welcome to Great Explorers!
This general elective course is open to students from all schools, programs, and years. The language of instruction is English.
The course's first lecture is on Sunday, March 15, 2026, 17:30 - 19:00, on zoom,
Here is the course syllabus, which includes the lectures schedule. It's important to consult this website before each lecture, for lecture materials and up-to-date zoom links (which may change during the semester).
April 12: Two Lectures
On April 12 we'll have two lectures, back to back. The first lecture will complete the AI overview that we started on March 22. The second lecture will give an overview of Polar Explorations.
Both lectures will be given in zoom only: https://runi-ac-il.zoom.us/j/86585419308
Here are the First lecture slides (AI, 17:30 to 19:00)
Here are the Second lecture slides (Polar Explorations, 19:15 to 20:45)
AI Readings (optional):
The Illustrated Transfomer: By Jay Alammar, an AI engineer and author. Explains the key techniques that drive Chatbots and generative AI tools. It is recommended to focus on the main ideas and ignore the technical details.
Deep Reinforcement Learning: Pong from Pixels. (for technically inclined students): By Andrej Karpathy, co-founder of Open AI and former AI Director in Tesla. Here, too, you may focus on the main ideas and ignore the mathematical parts.
Polar expolrations reading (which, as usual in this course, you have to read after the lecture, and before the next one):
(Start by going over the reading guidelines)
First Attempt to the South Pole: this text is taken from the book "Shackleton" by Roland Huntford. The first section in the text describes some general details about diet and dogs in polar explorations. The rest of the text describes an early attempt to reach the south pole, in 1902. The team included Scott (leader), Shackleton (a young ambitious explorer who will be the subject of a later lecture in this course), and Wilson, a highly competent polar explorer. The text illustrates the problematic character and leadership style of Scott, and describes the unique British approach to polar exploration.
Optional Polar Explorations resources:
Video clip about Captain Scott (6 minutes): illustrates the hero status that Scott attained in the British public eye.
Farthest North (by Fridjtof Nansen): written by the pioneer of modern polar exploration, this book describes an early epic attempt to reach the North Pole.
Kabloona (by Gontran de Poncins): the author spent several years among the Inuit (Eskimo) people of the Arctic, and wrote an empathic rendition of the Inuit lifestyle, courage and stamina. This is a rare book, available only from used book sellers. However, it is a gem. If you buy it, try to purchase the hardcover version, which includes beautiful water color paintings by the author.
The Last Gentleman Adventurer (by Edward Beauclerk Maurice): if you read one book about life with the Inuit people, this is it. A funny and heart breaking memoire written by an Englishman who, at age 16, was sent to man a trading post in one of the most remote places on the globe. This book is also available in Hebrew.
March 22: AI: Foundations
This lecture will be given in zoom only: https://runi-ac-il.zoom.us/j/87242608461
(This lecture will be also be attended by many first-year students who are now taking a course named DNAI. Note that the zoom link is different than the normal link of the Great Explorers course. I will start the lecture with a few words in Hebrew, and then switch to English).
Here are the Lecture Slides.
Homework: Here are some questions about what was learned in the lecture. Each question provides references to some additional 15-minute reading. Pick two questions and answer them, using no more than one page. This is a self-study exercise, there is no need to submit it.
The readings below are optional. They are aimed at students who want to get a solid understanding of Machine Learning.
Reading 1: “Why Machines Learn”, by Anil Ananthaswamy. The book's title is strange, but the contents are brilliant. Start by reading the first two chapters. Chapter 1 discusses the architecture and performance of a single artificial neuron, also known as Perceptron. Chapter 2 is an elegant introduction to basic Linear Algebra artifacts (vectors and matrices) that are at the very heart of the theory and practice of Artificial Intelligence and Machine Learning. There is no way to understand AI/ML deeply without some mathematical background. This reading provides a gentle (but non-trivial) introduction to the key mathematical ideas underlying AI and ML, aimed at readers without previous mathematical knowledge beyond basic high school math. If you don't have access to RUNI's digital library, you can access most of this reading here.
March 22: AI: Foundations
This lecture will be given in zoom only: https://runi-ac-il.zoom.us/j/87242608461
(This lecture will be also be attended by many first-year students who are now taking a course named DNAI. Note that the zoom link is different than the normal link of the Great Explorers course. I will start the lecture with a few words in Hebrew, and then switch to English).
Here are the Lecture Slides.
Homework: Here are some questions about what was learned in the lecture. Each question provides references to some additional 15-minute reading. Pick two questions and answer them, using no more than one page. This is a self-study exercise, there is no need to submit it.
The readings below are optional. They are aimed at students who want to get a solid understanding of Machine Learning.
Reading 1: “Why Machines Learn”, by Anil Ananthaswamy. The book's title is strange, but the contents are brilliant. Start by reading the first two chapters. Chapter 1 discusses the architecture and performance of a single artificial neuron, also known as Perceptron. Chapter 2 is an elegant introduction to basic Linear Algebra artifacts (vectors and matrices) that are at the very heart of the theory and practice of Artificial Intelligence and Machine Learning. There is no way to understand AI/ML deeply without some mathematical background. This reading provides a gentle (but non-trivial) introduction to the key mathematical ideas underlying AI and ML, aimed at readers without previous mathematical knowledge beyond basic high school math. If you don't have access to RUNI's digital library, you can access most of this reading here.
Reading 2: “Machine Learning Yearning”, by Andrew Ng, a leading Machine Learning educator, researcher, and practitioner. This freely available digital book assumes basic knowledge of AI and ML concepts and theory. It is recommended to start reading it after the next AI lecture, on April 12.
March 15: Explorations in Mathematics
We normally discuss explorations in Math, Computer Science, and AI later in the course. This year we'll do so at the beginning of the course, for reasons that will be explained in the lecture.
This lecture will be given in zoom only: https://runi-ac-il.zoom.us/j/86347129284
Here are the Lecture Slides.
More resources
Computer Science education in a nutshell. Pay special attention to minutes 11:00 onward, where I talk about early age mathematics education.
1 + 2 + 3 + 4 + ... = -1/12: This bizarre result can be derived correctly, if you are not going to the infinite, on the one hand, and you are not willing to say when you stop adding, on the other. A striking illustration of the elusive nature of what we call "infinity".
Optional reading
About Compass and Straightedge (Hebrew): An article about their role in mathematics.
Three great books about the spirit and beauty of mathematics:
Journey Through Genius, by William Dunham,
Men of Mathematics, by E. T. Bell
Number: the Language of Science, by Tobias Dantzig