MS in Artificial Intelligence and Machine Learning

Beginning fall 2020, Drexel’s new, 45-credit Master of Science in Artificial Intelligence and Machine Learning (MSAIML) program offers students an opportunity to learn a variety of foundational, computational, and applied topics in artificial intelligence and machine learning.

Drexel's Master’s in AI and Machine Learning provides students with core knowledge in algorithms, mathematics and applications, that can be applied, immediately in the professional workplace, or used as a launching point for more in depth and expansive knowledge or specialized careers in the field. As such, this graduate program is ideal for professionals who are interested in building on their existing quantitative skills to become leaders in the field of artificial intelligence and machine learning.

As one of a few select academic colleges across the nation that houses computer science and information science under one roof, the College of Computing & Informatics offers a cross-disciplinary, hands-on education to prepare students to meet industry demands for artificial intelligence and machine learning skills.

Master's in AI & Machine Learning Program Features

  • Explores the discipline's fundamental mathematics, developing related tools, and applying AI and ML to various real-world problems
  • Coursework covers a broad, interdisciplinary range of topics, including data science, both theoretical and applied artificial intelligence and machine learning, mathematics and algorithms for artificial intelligence and machine learning, and domain-specific applications of artificial intelligence and machine learning
  • Taught by CCI’s world class faculty who have active research experience in machine learning, computer vision, game AI, data science, cognitive science, high performance computing, software engineering, applied machine learning in gaming, and applied machine learning in security
  • Students have the option to pursue a dual degree program combining the MSAIML with any other CCI graduate degree program

The Master of Science in Artificial Intelligence & Machine Learning is Available

  • Full-time
  • Part-time
  • On Campus
  • Online

MS in AI & Machine Learning Curriculum

MS in Artificial Intelligence (AI) & Machine Learning (ML) coursework explores the artificial intelligence and machine learning field’s fundamental mathematics, while enabling students to develop related tools and apply AI and ML to various real-world problems. Students first learn AI and ML foundational concepts in completing five core required courses. Then, students may choose ten electives to further enrich their degree, while gaining hands-on experience with state-of-the-art tools and systems, and working with real datasets. The program culminates in a collaborative team capstone project, providing students with real-world practice in the applications of artificial intelligence and machine learning concepts before graduation.

This interdisciplinary, 45-credit, 15-course program includes:

  • Five required core courses;
  • Three elective courses, one within each of the following focal areas: Data Science and Analytics, Foundations of Computation and Algorithms, or Applications of Artificial Intelligence and Machine Learning;
  • Seven free elective courses may be selected from the above focal areas or Computer Science Department-approved courses;
  • A capstone course: students work in teams to pursue an in-depth, multi-term capstone project applying computing and informatics knowledge in an artificial intelligence project, spanning two quarters (6 credits).

Sample Courses in the MS in AI & Machine Learning Program:

  • CS 510 Artificial Intelligence: Well-formed problems; state spaces and search spaces; Lisp and functional programming; uninformed search; heuristic search; stochastic search; knowledge representation; propositional logic; first order logic; predicate calculus; planning; partial order planning; hierarchical planning.
  • CS 613 Machine Learning: This course covers the fundamentals of modern statistical machine learning. Lectures will cover fundamental aspects of machine learning, including dimensionality reduction, overfitting, ensemble learning, and evaluation techniques, as well as the theoretical foundation and algorithmic details of representative topics within clustering, regression, and classification (for example, K-Means clustering, Support Vector Machines, Decision Trees, Linear and Logistic Regression, Neural Networks, among others). Students will be expected to perform theoretical derivations and computations, and to be able to implement algorithms from scratch. The course will conclude with a final project and presentation on a machine learning problem of their choosing.
  • CS 615 Deep Learning: Introduces a machine learning technique called deep learning and its applications, as well as core machine learning concepts such as data set, evaluation, overfitting, regularization and more. Covers neural network building blocks: linear and logistic regression, followed by shallow artificial neural networks and a variety of deep networks algorithms and their derivations. Includes implementation of algorithms and usage of existing machine learning libraries. Explores the usage of deep learning on a variety of problems including image classification, speech recognition, and natural language processing. Concludes with student-chosen project demonstrations accompanied by a conference-style paper.
  • CS 591-592 Artificial Intelligence and Machine Learning Capstone I–II (6 credits, two courses): Explores artificial intelligence in practice as an open-ended team activity. Initiates and completes an in-depth multi-term capstone study applying computing and informatics knowledge in an artificial intelligence project. Teams work to develop a significant product with advisors from industry and/or academia. Explores artificial intelligence-related issues and challenges involved in the application domain of the team’s choice. Applies a development process structure for project planning, specification, design, implementation, evaluation, and documentation.

AI & Machine Learning Certificate Options

The MS in Artificial Intelligence & Machine Learning requires incoming students to have some prior math and programming concepts. For those without a computer science background, the Post-Baccalaureate Certificate in Computer Science provides an efficient and systematic education on the basics of computer science without any prerequisite knowledge. The certificate program may also serve as an on-ramp to the MS in Artificial Intelligence and Machine Learning, MS in Computer Science, or MS in Software Engineering programs, if completed with predetermined grade requirements. Students who advance to the MSAIML program from a completed Drexel Post-Baccalaureate Certificate in Computer Science will be given the option of transferring up to 12 credits from their certificate towards their MSAIML degree.

For those working in the tech field who are seeking to broaden or update their professional skills, Drexel CCI offers a Post-Baccalaureate Certificate Program in Artificial Intelligence & Machine Learning (enrolling fall 2020).

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