This course provides a comprehensive and practical background for students interested in continuous mathematics for computer science. The goal is to prepare students for higher-level subjects in artificial intelligence, machine learning, computer vision, natural language processing, graphics, and other topics that require numerical computation. This course is intended students who wish to pursue these more advanced topics, but who have not taken (or do not feel comfortable) with university-level multivariable calculus (e.g., MAT 201/203) and probability (e.g., ORF 245 or ORF 309). See "Other Information"

Introduction to Data Science provides a practical introduction to the burgeoning field of data science. The course introduces students to the essential tools for conducting data-driven research, including the fundamentals of programming techniques and the essentials of statistics. Students will work with real-world datasets from various domains; write computer code to manipulate, explore, and analyze data; use basic techniques from statistics and machine learning to analyze data; learn to draw conclusions using sound statistical reasoning; and produce scientific reports. No prior knowledge of programming or statistics is required.

A project-based seminar course in which students work individually or in small teams to tackle data science and machine learning problems, working with real-world datasets. The course emphasizes critical thinking about experiments and large dataset analysis and the ability to clearly communicate one's research. This course is intended to support students in developing the analytical skills necessary for quantitative independent work; students should consult with their home department about how this course could appropriately complement, but not replace, their independent work requirements.

Project-based course in which students work individually/small teams to tackle DS and ML problems, working with real-world datasets.The course emphasizes critical thinking about experiments and dataset analysis and the ability to clearly communicate one's research. Programming components are taught in Python. Experience in only one of the two programming languages (R and Python) is required.This course is intended to support students in developing the analytical skills for quantitative independent work; students should consult with their home department about how this course could complement, not replace, their independent work requirements.

This course offers an introduction to deep learning, which is the core technology behind most modern AI applications, aimed at students with minimal coding experience/mathematical background. Emphasis will be placed on gaining a conceptual understanding of deep learning models and on practicing the basic coding skills required to use them in simple contexts. By the end of the course, students will be able to understand, code and train a variety of basic deep learning models, including basic neural nets, image recognition models, and natural language processing models. As a capstone, students will build their own tiny GPT-style text generator.

This course is for graduate students enrolled in the CSML Graduate Certificate Program and is part of the certificate requirements. Students enrolled in the certificate must enroll, attend and present their research during at least one semester. Each week features a presentation by a student, invited faculty or external visitors. All students are required to read materials prior to the workshop and come prepared to engage in conversation. Each week a student presents, a second student introduces the speaker and gives background on the work and a third student moderates the post-presentation discussion.