ITCS 6156: Machine Learning

University of North Carolina, Charlotte

Introduction of advanced concepts, techniques, and algorithms underlying the theory and practice of machine learning (ML) and deep learning. The description of the formal properties of the algorithms will be supplemented with motivating applications in areas such as natural language processing, computer vision, education, or medicine. Topics include: bias-variance trade-off; ensemble methods; autoencoders, convolutional neural networks, (gated) recurrent neural networks and attention; Transformer and language models; probabilistic graphical models and structured outputs; energy-based models, GANs, VAEs, and diffusion; time series forecasting; explainability and probing of ML models; bias in ML models; theoretical underpinnings of deep learning; deep reinforcement learning (RL).

Average GPA: 3.64

Grade distribution records: 1,233 students across 26 terms.

Grade distribution

Based on 1,233 student grade records across 26 terms and 15 professors.

Instructors

• Minwoo Lee 358 students, Average GPA 3.67
• Atif Farid Mohammad 332 students, Average GPA 3.87
• Wlodek Zadrozny 120 students, Average GPA 3.50
• Nadia Najjar 75 students, Average GPA 3.52
• Amirmohammad Rooshenas 65 students, Average GPA 3.53
• Constantin Bunescu 64 students, Average GPA 3.50
• Xiuxia Du 64 students, Average GPA 3.61
• Richard Souvenir 45 students, Average GPA 3.08
• Hongfei Xue 33 students, Average GPA 3.30
• Robert Abbott 21 students, Average GPA 3.48
• Pamela Thompson 20 students, Average GPA 3.80
• Christian Kuemmerle 17 students, Average GPA 3.23
• Mirsad Hadzikadic 15 students, Average GPA 3.36
• Agnieszka Dardzinska 2 students, Average GPA 4.00
• Kyle Kreth 2 students, Average GPA 4.00