## Weeks 1-2: Mathematics of Data Science

- Topic 1: Probability theory - sample space, distributions, central limit theorem, Bayes theorem
- Topic 2: Linear Algebra - Vectors, Matrices as linear transformations, Orthogonality, Eigenspace, SVD
- Topic 3: Optimization - convex sets, functions, algorithms, gradient, steepest descent

## Week 3: Statistics for Data Science

- Topic 1: Hypothesis testing
- Topic 2: Confidence intervals, and p-values, power and significance
- Topic 3: Bayesian statistics
- Topic 4: Modeling, metrics, cross-validation

## Week 4-9: Techniques/Algorithms

- Topic 1: Linear and logistic regression
- Topic 2: Classification - naive Bayes, decision trees, random forests
- Topic 3: Clustering - k-means

## Week 10: Gradient Boosting

- Topic 1: Bagging, boosting
- Topic 2: XGBoost
- Topic 3: LightGBMs

## Week 11: Convolutional Neural Networks

- Topic 1: Pooling
- Topic 2: Batch Norm
- Topic 3: Applications

## Week 12: Overview - NLP and transformers

- Topic 1: Transformers architecture
- Topic 2: Traditional NLP topics
- Topic 3: Working with modern LLMs