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 |
(Replace this table with the actual schedule once finalized.)
Materials
- Slides:
- Notebooks:
- Readings:
Assessment
- Homework:
- Project:
- Final exam:
Schedule & Office Hours
- Lectures:
- Office hours:
- Contact: