Math 5750/6880: Mathematics of Data Science
Lecture 1: Introduction and Linear Models for Regression [Slides]
Lecture 2: Linear Models for Classification [Slides]
Lecture 3: Support Vector Machine and Large Margin Learning [Slides]
Lecture 4: Kernel Methods [Slides]
Lecture 5: Gradient Descent [Slides]
Lecture 6: Subgradient Methods [Slides]
Lecture 7: Proximal Gradient Methods [Slides]
Lecture 8: Accelerated Gradient Methods [Slides]
Lecture 9: Stochastic Gradient Descent [Slides]
Lecture 10: Curses, Blessings, and Surprises in High Dimensions [Slides]
Lecture 11: Graphs, Networks, and Clustering [Slides]
Lecture 12: Dimension Reduction [Slides]
Lecture 13: Feed-forward Neural Networks and Backpropagation [Slides]
Lecture 14: Recurrent Neural Networks and Continuous-depth Neural Networks [Slides]
Lecture 15: Self-attention Mechanism and Transformers [Slides]
