Machine Learning with Graphs
This course provides an in-depth look at how intricate relationships between objects can be represented as networks, crucial for modeling systems across social, technological, and biological domains. It tackles the computational and algorithmic challenges of massive graphs, guiding students through advanced concepts like representation learning, Graph Neural Networks, and algorithms designed for the World Wide Web. Participants will also delve into reasoning with Knowledge Graphs, strategies for influence maximization, and methods for detecting disease outbreaks and analyzing social networks. By leveraging machine learning techniques and data mining tools, students are primed to extract critical insights from complex network structures.
Tenure: 10 weeks (October 7 to December 15, 2024)
Course Eligibility: For those willing to sign up for this advanced course, a foundational understanding of computer science principles is essential.
Before starting your first course in the AI Professional Program, you must complete a brief 15-minute application to verify your readiness. This includes demonstrating proficiency in Python, as assignments will be conducted in this language, and some familiarity with basic Linux command line operations. You should also have a solid foundation in college-level calculus and linear algebra, including the ability to handle multivariable derivatives and matrix/vector operations. Additionally, a good understanding of probability theory is required, covering fundamental distributions such as Continuous, Gaussian, and Bernoulli, as well as concepts like Expectation, Independence, and both Probability and Cumulative Distribution Functions. A solid grasp of linear algebra is also recommended.
Career Prospects: In the AI-driven world, this course will open gateways to exciting opportunities in machine learning and artificial intelligence. Students learn to develop models that can predict and influence network behavior, a skill highly sought after in tech industries, including roles in AI research, data science, and cybersecurity.
Public Affairs Data Journalism
Before the advent of the internet and powerful computers, public accountability reporting depended heavily on meticulous data collection and analytical problem-solving. This course examines the methodologies and data sources essential for uncovering leads and performing comprehensive investigations into public matters, such as election finance and safety regulations. Students will acquire hands-on experience with digital tools and techniques used in computer-assisted reporting.
Tenure: 10 weeks (September 9 to December 6, 2024)
Course Eligibility: This course is exclusively available to students pursuing a Master’s in Journalism.
Career Prospects: This course is ideal for aspiring journalists, media analysts, and data-driven content creators, offering career opportunities in news organizations, digital media companies, and investigative journalism.
Startup Garage: Design
Startup Garage is a dynamic, hands-on course that immerses students in the world of entrepreneurship through real-world projects. Leveraging human-centric design, lean startup principles, and the Business Model Canvas, participants are tasked with developing and testing innovative business ideas that meet actual market needs.
Teams step out of the classroom to engage directly with users, industry experts, and advisors, gaining deep insights into customer challenges. They then design, prototype, and refine their products or services and business models. For those focused on impact-driven ventures, the same methodologies are used to address the needs of their target communities. Offered by the Graduate School of Business, this course equips students with essential entrepreneurial skills and cutting-edge techniques for launching successful ventures.
Tenure: 10 weeks (September 9 to December 6, 2024)
Course Eligibility: This is a graduation-level course.
Career Prospects: This course guides students through the process of identifying market needs, designing innovative solutions, and developing business models. Graduates of this course are well-prepared to launch their startups, join venture capital firms, or work in innovation roles within established healthcare companies.
Biological Macromolecules
This course delves into the physical and chemical principles underlying macromolecular functions. It examines the forces that stabilize macromolecular structures and their complexes, and explores the thermodynamics and statistical mechanics of processes such as folding, binding, and allostery. The course also covers diffusional processes and the kinetics of enzymatic reactions, linking these concepts to practical applications in experimental design and interpretation. Emphasizing an interactive learning approach, the course combines lectures with group problem-solving activities and discussions of contemporary and foundational literature.
Tenure: 10 weeks (September 23 to December 6, 2024)
Course Eligibility: A background in biochemistry and physical chemistry is recommended, though materials are provided for those who need additional support. Undergraduates may enroll with the instructor’s permission.
Career Prospects: Graduates of this course can pursue diverse career paths, including roles as biochemists, molecular biologists, and pharmaceutical researchers, where they apply their knowledge of macromolecular functions to research and development. They may also work as biotech scientists, proteomics specialists, or structural biologists, focusing on drug development, protein analysis, and structural biology. Additionally, opportunities exist in bioinformatics and academia, where they can contribute to data analysis and academic research in molecular biology and biochemistry.
