Course Description

This course will introduce students to the exciting ways Artificial intelligence (AI) is revolutionizing medicine and transforming the healthcare industry.

Students will work on practical projects that apply AI to real-world healthcare scenarios. For example, they will use CNNs to build image classifiers that can detect tumors in medical images, such as X-rays and MRIs, learning how AI assists doctors in making accurate diagnoses. They’ll also dive into Natural Language Processing (NLP) with Large language Models like BERT and ChatGPT, using these techniques to analyze medical texts—such as Electronic Health Records (EHRs)—to automate and streamline the way patient information is processed.

Throughout the course, we’ll also explore important ethical questions, like how to ensure data privacy and reduce biases in AI models. These discussions will help students think critically about the responsibilities that come with using AI in healthcare.  Students will gain hands-on experience working with healthcare datasets, building and fine-tuning AI models with popular programming tools like Python, TensorFlow, and PyTorch. They'll develop an understanding of how AI can support healthcare providers in making faster, more accurate decisions. Through these projects, students will also learn about the ethical considerations in AI, equipping them with a balanced view of its potential and limitations.

This course provides a unique opportunity for students to explore the intersection of technology and medicine, empowering them with practical skills and a forward-looking perspective on how AI can improve healthcare.

Learning Outcomes

By the end of the course, students will:

• Understand how AI is transforming healthcare, particularly in diagnostics and predictive analytics.
• Gain hands-on experience with advanced AI techniques, including CNNs and transformer models, applied to real-world healthcare datasets.
• Analyze the ethical implications of using AI in healthcare and propose strategies to ensure ethical model development.
• Develop programming proficiency in Python for healthcare AI applications.
• Work on projects related to medical imaging and text analysis, presenting their findings at the end of the session.

Prerequisites

High school algebra, and some familiarity with coding concepts (e.g., basic experience with any programming language such as Python, Java, or C++). 

Course Description

This course is for students interested in basic understanding and familiarity with the elements and procedure of the American criminal justice system. The material will introduce students to the building blocks of criminal statutes, their essential items to be proven in a criminal case, and a familiarity to the basic outline and structure of the American criminal process from investigation to trial and appeal, as well as civil rights and constitutional safeguards intrinsic to the present-day system.

For a final project, students will choose or volunteer for assignment as role players in demonstrative scenario sketches of direct and cross examination of witnesses; jury selection; interrogation of suspects, and perhaps mock appellate moot court if time permits. Alternatively, students might write a brief research essay on selected issues in the course, or a short expositive legal brief.

Learning Outcomes

By the end of this course, students will:

• Understand the dual necessity of criminal intent and guilty acts in combination to comprise traditional common law crimes, and the procedural requirements and safeguards in the constitutional American system of justice.
• Be able to recognize the elements of due process for defendants, and all citizens, in investigations, interrogations, searches and seizures, and the right to counsel, as well as basic due process guarantees in trials and other court room proceedings.

Prerequisites

A middle or high school course in government or civics is helpful but not required. 

Course Description

This course is for students who are fascinated by the brain. A concussion, or mild traumatic brain injury (mTBI), can be life-altering. It can lead to permanent brain damage or worse. However, concussions can also heal quite rapidly, even with minimal intervention. When the brain is damaged due to concussions, a number of systems may be affected including balance, vision and cognition. In this course, students will learn fundamental principles of neuroscience by examining the cognitive and behavioral changes that can result from concussions. The course will include:

• An overview of neuroanatomy and neural communication
• How brain damage impacts thoughts, feelings, and behavior
• Emerging diagnostics and rehabilitation strategies for brain injury

For their capstone project, students will work in groups of two or three on presentations on a topic of their choice related to concussions, such as latest rehabilitation techniques, diagnostic tools, or other topics of interest. 

Learning Outcomes

By the end of this course, students will be able to:

• Identify key brain pathways structures and functions
• Describe new advances in concussion screening and treatment
• Explain brain-behavior relationships

Prerequisites

None

Course Description

This two-week experiential course is ideal for high school students interested in building business skills and becoming conversant in key business concepts such as economics, finance, accounting, strategy and marketing. Project work takes place in smaller teams, thus modeling the teamwork aspect of the modern workplace, and providing the participants the opportunity to build or enhance collaboration and leadership skills. We will also consider how some of the concepts and methodologies used in business apply to our daily lives and decision-making.

In addition to instruction and discussion of real-life case studies, the hands-on valuation project is at epicenter of the course. Through the valuation project, we will explore how real companies think about clients and customers, how they make money and finance themselves, how they think about competition, innovation, and new projects, and how those decisions affect their value. The project culminates with a group presentation of project findings and analyses. 

Learning Outcomes

Upon completing this course, participants will:

1. Have a solid grasp of concepts in areas such as economics, finance, strategy, and accounting.
2. Be conversant in the ways businesses evaluate and make decisions about new projects.
3. Understand the effects of compounding, (financial) leverage, and risk on expected returns.
4. Be able to apply financial analysis methodologies to issues of personal finance.
5. Be able to discuss the differences between breakthrough and disruptive innovation and identify relevant opportunities.
6. Practice teamwork and collaboration on a real-life problem.
7. Feel more confident about creating and delivering persuasive presentations.
8. Have explored potential career areas in business. 

Prerequisites

None

Course Description

Creative writing is a process of exploration and discovery. This creative writing workshop offers an intensive opportunity for eager and motivated students to investigate ideas and reflect on their experiences. Students will be challenged in both fiction and nonfiction, through short stories, nonfiction narratives, and personal essays, to produce work that will be critiqued by the professor with the aims of developing their skills in written expression and creating tools for critical evaluation of their writing. Students’ work will also be shared with the class in small groups where methods for constructive criticism will be developed and applied. In the writing workshops, personal attention will be given to each student. In addition, we’ll read excerpts from exemplary writers, and guest speakers will talk to us about their own development. The aim is to become more insightful writers, more perceptive readers, more confident students, and even more excited about writing. 

Learning Outcomes

• Greater motivation and confidence about writing, and greater enjoyment
• Commitment to keep challenging themselves
• Greater understanding of the skills needed to improve and develop no matter what the level of achievement and ambition
• Deeper appreciation for the writing process and openness to what others write.

Prerequisites

None.

Course Description

Data Science is a multidisciplinary field that blends data inference, algorithm development, and technology, transforming raw data into meaningful insights and innovations. This course introduces high school students to this critical and burgeoning field. Emphasizing both quantitative analysis and qualitative interpretation, the program begins with Python programming fundamentals and advances through key concepts like data structures, manipulation, and exploratory data analysis (EDA). A special focus on Natural Language Processing (NLP) highlights the interdisciplinary nature of data science, integrating computational methods with linguistic insights. Students will engage in hands-on projects, delve into real-world datasets, and acquire skills to convert data into compelling stories and actionable intelligence. This course is a gateway into the expansive world of data science, where machine learning, artificial intelligence, and big data are pivotal tools in shaping our future.

Learning Outcomes

1. Proficiency in Python for Data Science: Gain hands-on experience in Python, focusing on its application in data science, including understanding data structures, and libraries like Pandas and NumPy.
2. Fundamentals of Data Analysis and Visualization: Perform exploratory data analysis (EDA), interpret data through statistical methods, and create meaningful visualizations using tools like Matplotlib and Seaborn.
3. Introduction to Natural Language Processing (NLP): Learn to process and analyze text data, including text manipulation, sentiment analysis, and creating visual representations like word clouds.
4. Execution of Data Science: Work on exercises aligned with each day's topic, culminating in a project where students reflect on their discoveries.
5. Critical Thinking and Problem-Solving in Data Science: Develop critical thinking skills specific to data science, learning to approach problems analytically, question assumptions, and interpret results within context. 

Prerequisites

None

Course Description

Animals engage in an array of fascinating behaviors, like hunting, foraging, mating, and migration. Neuroscientists are particularly interested in the link between behavior and the brain. In this course we will ask questions such as, how do bats echolocate? Does birdsong give us insight into how the human brain acquires language? What can a crab teach us about nervous system plasticity? We will relate these behaviors to the core principles of electrical and chemical transmission in the nervous system. This course will also introduce students to overarching concepts of computation by neurons, like rate coding, receptive fields, and feature detection. Students will engage with this material through a combination of readings, video content, in-class activities, and hands-on experiments. 

As a capstone project, students will form small groups (~3 students per group) to investigate a topic of their own choosing (with feedback from the instructor) in the area of animal behavior and neuroscience. Each group will conduct a scientific literature search and give a brief presentation  at the end of the course.

Learning Outcomes

At the conclusion of this course, students will be able to:

• Identify foundational ideas in animal behavior (ethology).
• Identify mechanisms of action of the nervous system and how they relate to behavior in the model organisms that we studied.
• Appreciate the wide diversity of animal behavior and the corresponding diversity of nervous systems in the animal kingdom.
• Identify and evaluate key findings from influential experiments in the field of behavioral neuroscience and what they tell us about human brain function and behavior. 

Prerequisites

A course in high school biology (preference for Honors or AP Biology, but not required). A course in Algebra.

Course Description

Artificial Intelligence (AI) has gained significant attention with tools like ChatGPT and other Large Language Models (LLMs). However, AI’s roots go much deeper, with methods developed over decades to solve specific, complex challenges like Sudoku, Chess, and even Wordle. Inspired by the popular CS176 course taught by the  Computer Science Department, this course is designed for students eager to explore these foundational AI techniques, from classical search algorithms like breadth and depth-first search to an introduction to logic. The course includes:

• An introduction to classical AI methods including Search, Constraint Satisfaction Problems, Minimax, propositional and predicate logic, Bayesian networks.
• Development of problem-solving skills by applying these AI methods to toy problems including famous games and logic puzzles.
• Exploration of real-world applications by applying these techniques to fields like robotics, economics, and genomics.

Learning Outcomes

By the end of this course, students will:

• Have a foundational understanding of classical AI methods.
• Be able to implement basic AI algorithms in Python to solve problems and take away a collection of useful code templates to revisit after the course.
• Gain a clear understanding of the applications and limitations of classical AI in real-world scenarios, along with an appreciation for how the current excitement around LLMs fits into the broader history of AI.

Prerequisites

Basic knowledge of programming (Python preferred). Basic probability equivalent to that of AP Statistics would be helpful but not required.