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ML 2023
Introduction to ML
- HW1
- HW2
Deep Learning 2024
ML 2024 (MS)
- HW1
ML 2024 (MBA)

Classical ML

Introduction
Data
Supervised learning
Linear regression
Linear classification
Evaluation Metrics
Probabilistic models
Decision Trees
Ensembling
Gradient boosting
Unsupervised Learning

Deep Learning

Multilayer perceptron (MLP)
CNN
Sequential NNs
- Vanilla RNN
- LSTM
- GRU
- Attention
- Transformers
Generative models
Reinforcement Learning

Python for ML

Python Basics
- Basic Types
- Variables
- Control flow
- Functions
- Classes
- ISLP Lab
NumPy
Pandas
Data visualization

Math for ML

Linear Algebra
Calculus
Probability
Statistics
- Limit theorems
- Estimations

References

Notation
Bibliography
ML resources

.ipynb

Regularization in MLP

Contents

Dropout
Batch normalization

Regularization in MLP#

Regularization in ML Handbook

Dropout#

https://media.licdn.com/dms/image/C4D12AQEgQmpFt-mI7Q/article-cover_image-shrink_720_1280/0/1595924298540?e=2147483647&v=beta&t=jfjUwRMl6awnN-QKfQjBz9l8Umoeip_PjE8RtHO5uqM

Batch normalization#

https://miro.medium.com/v2/resize:fit:1153/1*xQhPvRh08oKFC63swgWr_w.png

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Weights initialization

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Optimization in DL

Contents

Dropout
Batch normalization

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