# Syllabus

## Course Description

**AINS6003 Deep Learning & Neural Networks** explores neural network architectures—including multilayer perceptrons, convolutional, recurrent, and transformer models—with emphasis on training techniques, optimization, regularization, and applications in vision, speech, and generative AI. The course aligns with Aurnova **AIN6003** (3 credits, online, 8-week block).

## Prerequisites

- **AINS6002** Machine Learning & Predictive Modeling (or equivalent)
- Comfort with Python, linear algebra, and basic probability
- Access to GPU-backed compute for later modules (Codespace, Colab Pro, or institutional credits)

## Learning Outcomes

By the end of the course, students should be able to:

- explain forward and backward propagation in feedforward networks
- select optimizers, loss functions, and regularization for a given task
- design CNN, RNN/LSTM, and transformer-based solutions at a conceptual and implementation level
- compare discriminative and generative modeling approaches
- profile and document GPU training workflows for reproducibility

## Assessment Pattern

| Component | Weight |
|-----------|--------|
| Module assignments (6–8 submissions) | 50% |
| Lab notebooks (completion + reflection) | 20% |
| Mid-course architecture design brief | 15% |
| Final synthesis / capstone-style project | 15% |

## Weekly rhythm

Each module includes: overview, book prose, assignment, slides, narration, instructor notes, and a lab notebook.

## Resources

- [AIMA Python](https://github.com/aimacode/aima-python) — reference implementations
- [PyTorch tutorials](https://pytorch.org/tutorials/)
- Aurnova **MSAI-Proposal.md** — program-level outcomes for AIN6003

## Schedule (example 8-week block)

| Week | Module | Theme |
|------|--------|--------|
| 1 | 1 | Neurons, MLPs, activations |
| 2 | 2 | Backpropagation and autograd |
| 3 | 3 | Optimization and regularization |
| 4 | 4 | Convolutional networks |
| 5 | 5 | Sequence models |
| 6 | 6 | Attention and transformers |
| 7 | 7 | Generative models |
| 8 | 8 | GPU workflows and deployment |