Post-Baccalaureate Certificate in Artificial Intelligence and Machine Learning

CCI's new Post-Baccalaureate Certificate in Artificial Intelligence and Machine Learning program is designed for recent graduates and professionals who are interested in building on their existing quantitative skills to become leaders in the field of artificial intelligence and machine learning. The program may also serve as an entry point to Master of Science degrees in Computer Science, Data Science, or Artificial Intelligence and Machine Learning.

By combining foundational knowledge, applied training, and computational problem-solving knowledge, the AI and Machine Learning certificate program 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 in the field.

Artificial Intelligence and Machine Learning Program Requirements

Artificial Intelligence and Machine Learning Certificate Coursework

  • Two required core courses (6 credits):
    • 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.
  • Two electives courses (6 credits):
    • Sample elective courses: CS 511 Robot Laboratory; CS 618 Algorithmic Game Theory; DSCI 631 Applied Machine Learning; DSCI 691 Natural Language Processing with Deep Learning

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    Admissions Requirements

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