Teaching
Deep Learning
From the single perceptron and the Universal Approximation Theorem to the state of the art.
Universidad Panamericana · Master’s in Data Science
Course Description
A comprehensive journey through the foundations and state-of-the-art of Deep Neural Networks, from the mathematical basics of the Perceptron through Transformers and generative models.
Syllabus
| Week | Topic |
|---|---|
| 1 | Perceptron, Universal Approximation Theorem |
| 2 | Backpropagation and gradient descent |
| 3 | Optimizers: SGD, Momentum, Adam |
| 4 | Convolutional Neural Networks |
| 5 | Recurrent networks: RNN, LSTM, GRU |
| 6 | Attention and the Transformer architecture |
| 7 | Pretrained language models: BERT, GPT |
| 8 | Generative models: VAEs and GANs |
Provisional syllabus. The session schedule will be posted once finalized.
Materials
Slides, notebooks, and readings have not yet been posted.
Assessment
Assessment details and grading weights have not yet been posted.
Schedule & Office Hours
Lecture times and office hours have not yet been posted.
Contact: León Palafox