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Data Science Thesis and Causal Inference
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Module 1
Linear Algebra, Calculus, and Optimization for Machine Learning
1
Computing Linear Predictions with Vector Dot Products
Compute linear predictions and weighted sums using vector dot products.
2
Matrix Multiplication and Dimension Checking
Perform matrix multiplication while checking the dimensions of inputs and outputs.
3
Solving Linear Systems and Understanding Feature Redundancy
Solve a small system of linear equations and relate singularity to redundant features.
4
Diagnosing Exact Multicollinearity Using Matrix Rank
Use matrix rank to diagnose exact multicollinearity in a design matrix.
5
Converting Between Probabilities, Odds, Log-Odds, Exponentials, and Logarithms
Transform between probabilities, odds, log-odds, exponentials, and logarithms.
6
Differentiating Products and Compositions Using the Product and Chain Rules
Differentiate a scalar function using the product and chain rules.
7
Computing Gradients from Partial Derivatives
Compute a gradient from partial derivatives of a multivariable loss function.
8
Performing and Interpreting a Gradient Descent Update
Perform and interpret one gradient-descent parameter update.
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