Syllabus¶
1 - Data I¶
| Topic | Slides | Video | Optional Materials |
|---|---|---|---|
| Logistics | |||
| Course introduction | 1. Challenges in Deploying Machine Learning | ||
| Data acquisition | 1. Data Collection for Machine Learning |
2 - Data II¶
| Topic | Slides | Video | Optional Materials |
|---|---|---|---|
| Web scraping | |||
| Data labeling | |||
| Exploratory data analysis |
3 - Data III¶
| Topic | Slides | Video | Optional Materials |
|---|---|---|---|
| Data cleaning | |||
| Data transformation | |||
| Feature engineering | |||
| Data summary |
4 - ML model recap I¶
| Topic | Slides | Video | Optional Materials |
|---|---|---|---|
| ML overview | |||
| Tree methods | |||
| Linear methods | 1. Ch3 in D2L |
5 - ML model recap II¶
| Topic | Slides | Video | Optional Materials |
|---|---|---|---|
| Neural networks | 1. Ch4, Ch6, Ch8 in D2L |
6 - Model Validation¶
| Topic | Slides | Video | Optional Materials |
|---|---|---|---|
| Evaluation metrics | |||
| Underfitting and overfitting | |||
| Model validation |
7 - Model Combination¶
| Topic | Slides | Video | Optional Materials |
|---|---|---|---|
| Bias and variance | |||
| Bagging | |||
| Boosting | |||
| Stacking |
8 - Covariate Shift¶
| Topic | Slides | Video | Optional Materials |
|---|---|---|---|
| Generalization performance recap | |||
| Covariate shift |
9 - Covariate Shift II¶
| Topic | Slides | Video | Optional Materials |
|---|---|---|---|
| Covariate shift with more math | |||
| adversarial data and invariants |
10 - Label Shift¶
| Topic | Slides | Video | Optional Materials |
|---|---|---|---|
| Two sample test | |||
| Label shift |
11 - Data beyond IID¶
| Topic | Slides | Video | Optional Materials |
|---|---|---|---|
| Independence tests | |||
| Sequence models | |||
| Graphs |
12 - Model Tuning¶
| Topic | Slides | Video | Optional Materials |
|---|---|---|---|
| Model tuning | |||
| HPO algorithms | |||
| NAS algorithms |
13 - Deep Network Tuning¶
| Topic | Slides | Video | Optional Materials |
|---|---|---|---|
| Batch and layer norms | |||
| Residual connections | |||
| Attention |
14 - Transfer Learning¶
| Topic | Slides | Video | Optional Materials |
|---|---|---|---|
| Fine-tuning for CV | |||
| Fine tuning for NLP | |||
| Prompt-based learning |
15 - Model Compression¶
| Topic | Slides | Video | Optional Materials |
|---|---|---|---|
| Pruning and quantization | |||
| Knowledge distillation |
16 - Multimodal data¶
| Topic | Slides | Video | Optional Materials |
|---|---|---|---|
| Multimodal data |
17 - Fairness¶
| Topic | Slides | Video | Optional Materials |
|---|---|---|---|
| Examples | |||
| Law | |||
| Risk distributions | |||
| Criterias | |||
| In practice |
18 - Explainability¶
| Topic | Slides | Video | Optional Materials |
|---|---|---|---|
| Explainability | |||
| Strategies | |||
| Conditioning and backdoors | |||
| Axiomatic approaches | |||
| Heuristics |