Events

Berkeley’s Active Learning classrooms (ALC) are teaching and learning spaces that allow the instructor to customize the physical learning environment to meet pedagogical needs. In an ALC, the room design, flexible furniture, writing surfaces, and technology allow for multiple pedagogical activities. Research also suggests that students in ALCs outperform their peers in traditional classrooms (Cotner et al., 2013). ALCs have been intentionally designed to encourage and foster the use of active learning strategies - except, they don’t come with a manual about how to make that happen! In this workshop,  you’ll learn how to get the most out of your classes in ALCs. By the end of the workshop, participants will be able to: Describe what an Active Learning Classroom is Outline the advantages of an active learning classroom Identify learning strategies that offer students multiple ways to engage with the course content, their peers, and the instructor This session will run for 45 minutes, with an additional 15 minutes reserved for questions. This event will take place in-person at Social Sciences 110. Please RSVP if you're planning to join.  ➡️ Register for this event here!⬅️ ***Registration for this session will close one hour before the session***

The goal of this workshop is to build intuition for deep learning by building, training, and testing models in Python. Rather than a theory-centered approach, we will evaluate deep learning models through empirical results. We start with a review of what deep learning is and then unpack what neural networks are and how they work. We then jump straight into Python, using the Keras library to build neural networks. We will explore how different architectures affect the performance of predicting handwritten digit images. Lastly, we explore a specific flavor of neural networks, the convolutional neural network. We review how it’s different from a standard vanilla neural network, and build different architectures to test how well they perform on the classification of animal and vehicle image classification.