ITCS 8156: Machine Learning

University of North Carolina, Charlotte

Introduction to 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: 3.72

Grade distribution records: 176 students across 20 terms.

Grade distribution

Based on 176 student grade records across 20 terms and 11 professors.

Instructors

• Minwoo Lee 58 students, Average GPA 3.77
• Amirmohammad Rooshenas 25 students, Average GPA 3.74
• Nadia Najjar 19 students, Average GPA 4.00
• Richard Souvenir 16 students, Average GPA 3.31
• Christian Kuemmerle 15 students, Average GPA 3.53
• Mirsad Hadzikadic 12 students, Average GPA 3.75
• Constantin Bunescu 10 students, Average GPA 4.00
• Wlodek Zadrozny 9 students, Average GPA 3.25
• Hongfei Xue 7 students, Average GPA 3.71
• Atif Farid Mohammad 3 students, Average GPA 4.00
• Kyle Kreth 2 students, Average GPA 4.00