AI Ain’t Magic – Karishma Sekhon Edgar (Session 1) FULL
Artificial Intelligence (AI) is a captivating field that continues to evolve and transform our world. From self-driving cars to virtual assistants, it is making a significant impact in various aspects of our lives. But, what truly is AI, and what fuels the widespread enthusiasm surrounding it? This two-week course aims to illuminate these questions. Our journey begins with exploring the historical foundations of AI and progresses to a comprehensive understanding of its underlying mechanisms. The first week will be dedicated to gaining a solid foundation in Neural Networks (NN), a fundamental AI architecture. We’ll learn how NNs encode data, make predictions, and are trained. Furthermore, we will contrast AI with natural intelligence, examining the intricacies of both through examples ranging from simple organisms (slime molds!) to the intricacies of the human brain. After acquiring a robust grasp of Neural Networks and model training, the second week will focus on advanced AI architectures and their diverse applications. We’ll delve into the intricacies of renowned AI models like DALLE 2 and ChatGPT4, discussing their innovations and potential implications. Additionally, we will address ethical and copyright issues concerning AI and emphasize responsible uses of the technology. By the end of this course, you will walk away with a nuanced understanding of AI, as well as an appreciation for the fact that “AI ain’t magic” – it is meticulously designed models engineered to accomplish complex learning tasks.
CS Math: Truth, Proofs, and Impossibility – Emily Graetz (Session 3) FULL
This course will take you from the deepest foundations of mathematical truth to the farthest reaches of what is possible for logic. We will start by drilling down to what it means for an argument to be true, and how we can build complex ideas out of base axioms. The proof techniques you will learn are applicable all the way from debunking misleading statistics to solving high-level competitive math problems. We will introduce additional discrete structures as needed, such as sets, functions, and graphs. Later, we will introduce what a computer scientist means by an “algorithm” and how one can “solve a problem”. Finally, we will work our way to showing that there are some problems that cannot be solved by any algorithm. In the mornings, we will work together as a class to come up with definitions and techniques to solve challenging problems. Afternoons will be more open, with time spent working on problems in smaller groups or playing games with logical underpinnings. This course will only expect familiarity with techniques and concepts from Algebra, as it will build up its own branch of math, separate from Calculus. However, it will be fairly rigorous and will expect you to be open to learning this new way of looking at the world. This material is wild and strange; all questions are welcome, and there is no such thing as a stupid question.
Digital Media with Python – Katie Waddle (Session 1) FULL
Calling all artists, musicians, filmmakers, and programmers! In this course we will learn how to use code to manipulate and create text, images, sound, and video. How does a computer know what a photo is anyway? Or a sound? We’ll learn about how computers store the digital information of media, and then learn how to change it, adding cool effects, weird distortions, and wild beauty. We’ll talk about how human perception has shaped digital media design. You’ll work on several different creative projects in just two weeks, ultimately coding your own (very short) film. Along the way, you’ll pick up the basics of the Python programming language, one of the most common programming languages used in industry. This class is perfect for someone new to programming, or someone who knows a little programming and is interested in getting creative.
Modeling the Physical World – Ben Torralva (Session 3) FULL
Whether we are interested in designing and building the latest computer chips or Formula 1 racecars, or we wish to push the forefront of scientific understanding, computer modeling plays an essential role. In nearly all cases today, a computer model of the system is created. Sometimes the models are used to discover fundamental physics of the system. In other cases, they are used in the design and development process. Oftentimes, they are used to interpret and understand the results of tests and experiments. In this course, we will delve into the microscopic world. Our goal is to simulate the heating and melting of a solid copper crystal. We will first build the crystal one atom at a time. We will then use our computer model to simulate its heating and melting. The mathematical approximations and algorithms needed to simulate the dynamics of the interacting atoms will be developed as we progress. Surprisingly, the only math we will need is algebra. You will use the Python programming language to write your program; however, prior knowledge of Python is not necessary – we will learn the language as we go. It is only required that you have a basic understanding of how to use a computer and how to program at a rudimentary level in any programming language.
Your Journey Into Data Science with Python – Xian Zhang (Session 3) FULL
Unlock the power of data and discover how programming can transform raw numbers into meaningful stories. This immersive course introduces students to the exciting world of data science through hands-on Python programming. The course begins with an accelerated introduction to general programming in Python. Then we will focus on Python’s scientific computing stack: NumPy and SciPy. Lastly, we will create our own data-driven projects where we can learn to clean, analyze and visualize data. From understanding basic coding concepts (data structure and algorithm) to analyzing real-world datasets, you will develop the skills that are shaping the future of technology, research and decision-making.
