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ECE 5424 - Advanced Machine Learning (3C)

Course Description

Algorithms and principles involved in machine learning; focus on perception problems arising in computer vision, natural language processing and robotics; fundamentals of representing uncertainty, learning from data, supervised learning, ensemble methods, unsupervised learning, structured models, learning theory and reinforcement learning; design and analysis of machine perception systems; design and implementation of a technical project applied to real-world datasets (images, text, robotics).

Why take this course?

We are witnessing an explosion in data – from billions of images shared online to Petabytes of tweets, medical records and GPS tracks, generated by companies, users and scientific communities. Applications of machine learning and perception are increasing rapidly as more techniques are developed and implemented to address a wide range of scientific and societal problems. Many universities are expanding programs in machine learning and perception, and employers are increasingly recognizing the importance of such knowledge. Students trained in a deeper understanding of machine learning techniques will be better equipped to make fundamental contributions to research in machine learning, and applied areas such as perception (vision, text, speech), robotics, bioinformatics, etc.

Learning Objectives

  • 1. Analyze and contrast broad classes of machine learning algorithms (supervised vs. unsupervised vs. semi-supervised)
  • 2. Describe and apply fundamental concepts of learning from data (maximum likelihood estimation, maximum a posteriori, overfitting vs underfitting, regularization, cross-validation)
  • 3. Illustrate and program supervised learning algorithms for regression (e.g. least squares via pseudo-inverse)
  • 4. Illustrate and program supervised learning algorithms for classification (e.g. logistic regression via gradient descent, support vector machines via Quadratic Programming)
  • 5. Design and program unsupervised learning algorithms for clustering (e.g. kmeans)
  • 6. Design and implement a technical project, and apply developed techniques to real-world datasets (images, text, robotics, etc.)
  • 7. Apply and adapt learned machine learning techniques to their own research/thesis domain