ECE 4200

ECE 4200

Course information provided by the Courses of Study 2020-2021. Courses of Study 2021-2022 is scheduled to publish by July 1.

The course will be devoted to understanding, implementation, and applications of various machine learning primitives. This course is intended to have three modules, and within each we will cover basic theory, and implementations. The modules will be supervised learning, unsupervised learning, and finally topics that are motivated by engineering applications such as speech recognition, and recommendation systems. Supervised learning will include regression, support vector machines, decision trees, random forests, naïve Bayes, boosting and bagging. Unsupervised learning includes clustering, k-means, k-NN, principal components analysis and other dimensionality reduction methods. We will give particular emphasis on engineering applications, e.g., text data, hand-writing, music, image, and time series data, and categorical datasets such those in recommendation systems. The course will have a programming component, which will be administered in the form of assignments, and in-class-kaggle competitions.

When Offered Fall, Spring.

Permission Note Enrollment limited to: juniors, seniors and graduate students.
Prerequisites/Corequisites Prerequisite: MATH 2940, ECE 3100 or STSCI 3080 or ECE 3250 or equivalents.

View Enrollment Information

Enrollment Information
Syllabi: none
  •   Regular Academic Session.  Choose one lecture and one discussion. Combined with: ECE 5420

  • 4 Credits Stdnt Opt

  • 17545ECE 4200  LEC 001

    • TRTo Be Assigned
    • Aug 26 - Dec 7, 2021
    • Acharya, J

  • Instruction Mode: In Person

  • 17648ECE 4200  DIS 201

    • RTo Be Assigned
    • Aug 26 - Dec 7, 2021
    • Acharya, J

  • Instruction Mode: In Person