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 |