Course Details

01:198:461 Machine Learning Principles

  • Course Code: 01:198:461
  • Title: Machine Learning Principles
  • Short Description: This course is a systematic introduction to machine learning, covering theoretical as well as practical aspects of the use of statistical methods. Topics include linear models for classification and regression, support vector machines, regularization and model selection, and introduction to deep learning. Machine learning is a vast, fast-changing field. The course is designed to provide a solid starting point by focusing on technical foundations. Students will learn fundamental concepts in the field such as generalization and model selection. Students will also learn how to implement key techniques in the field from scratch including gradient descent, kernel trick, boosting, and expectation maximization. The course content will be delivered by a combination of written assignments that require rigorous proofs and self-contained Python Juypter Notebooks that require completing missing portions and empirically investigating the behavior of models on datasets. The course will reinforce learning with a series of short quizzes throughout the semester.The course content is designed to be accessible to all SAS students regardless of their majors who have an adequate background in linear algebra, calculus, probability, and programming.
  • Degree Type (BA/BS): BS in Data Science
  • Track Option: Computer Science Track (Code: NB219SJ)
  • Degree Option: BS in Data Science- Computer Science Track (Code: NB219SJ)
  • Prerequisite: M 250 (Introductory Linear Algebra), CS 112 (Data Structures), CS 206 (Introduction to Discrete Structures II) OR M 477 (Mathematical Theory of Probability) OR S 379 (Basic Probability Theory)
  • Learning Goals:

    The course objectives are: (1) understanding the goals, capabilities, and principles of machine learning, (2) acquiring mathematical tools to formalize machine learning problems, and (3) acquiring implementation skills to build practical machine learning systems. At the end of the course, students will have the knowledge of core machine learning techniques, so that they can use them to solve real-world prediction problems using basic Python and a small set of libraries. The knowledge they acquire in this course will directly transfer to any future courses they may take that go deeper into specific application areas of machine learning.