From de26dca032979288370186e0d117f874fa9fcda0 Mon Sep 17 00:00:00 2001 From: Jesse Perla Date: Fri, 13 Sep 2024 10:29:41 -0700 Subject: [PATCH] Updated schedule --- README.md | 43 +++++++++++++++++++++++++++++-------------- 1 file changed, 29 insertions(+), 14 deletions(-) diff --git a/README.md b/README.md index 717c3c8..930731f 100644 --- a/README.md +++ b/README.md @@ -68,7 +68,21 @@ Slides for the lectures can be found [here](https://ubcecon.github.io/ECON526/le - Introductory material on linear algebra: https://intro.quantecon.org/linear_equations.html and https://datascience.quantecon.org/scientific/applied_linalg.html - Matrix decompositions and other topics: https://python.quantecon.org/linear_algebra.html#further-topics -- **September 9**: Applications of Linear Algebra (Eigenvalues and Discounting) +- **September 9**: Continuing on Introduction to Numerical Linear Algebra + - **Topics:** Overview of computational complexity and numerical precision, solving systems of equations, geometric interpretations of linear algebra, matrix decompositions, linear least squares, and eigenvalues and eigenvectors. Preparation for applications. + - **Material:** + - [Linear Algebra Foundations](https://ubcecon.github.io/ECON526/lectures/lectures/linear_algebra_foundations.html), [Jupyter](lectures/lectures/linear_algebra_foundations.ipynb), [PDF](lectures/lectures/linear_algebra_foundations.pdf) + - **Self-study:** + - Basics of linear algebra, matrices, norms, and linear independence: https://python.quantecon.org/linear_algebra.html + - Numerical optimization: https://datascience.quantecon.org/scientific/optimization.html + - Systems of Equations: https://python.quantecon.org/linear_algebra.html#solving-systems-of-equations + - Eigenvectors and eigenvalues: https://python.quantecon.org/linear_algebra.html#eigenvalues-and-eigenvectors + - Downloading and manipulating data in Python: https://intro.quantecon.org/long_run_growth.html and https://intro.quantecon.org/business_cycle.html + - **(Optional) Extra Material**: + - Introductory material on linear algebra: https://intro.quantecon.org/linear_equations.html and https://datascience.quantecon.org/scientific/applied_linalg.html + - Matrix decompositions and other topics: https://python.quantecon.org/linear_algebra.html#further-topics + +- **September 11**: Applications of Linear Algebra (Eigenvalues and Discounting) - **Topics:** Geometric series and present values, difference equations, steady states, and convergence, unemployment dynamics, present discounted values - **Material:** - Finishing off [Linear Algebra Foundations](https://ubcecon.github.io/ECON526/lectures/lectures/linear_algebra_foundations.html) @@ -81,21 +95,21 @@ Slides for the lectures can be found [here](https://ubcecon.github.io/ECON526/le - Supply and Demand: https://intro.quantecon.org/intro_supply_demand.html - More on Competitive Equilibrium: https://intro.quantecon.org/supply_demand_multiple_goods.html -- **September 11**: Latent Variables and Intro to Unsupervised Learning - - **Topics:** Finished off eigenvalues and dynamics, principle components, and present discounted values +- **September 16**: Latent Variables and Intro to Unsupervised Learning + - **Topics:** Review eigenvalues and dynamics, principle components, and present discounted values - **Material:** - [Latent Variables and Unsupervised Learning](https://ubcecon.github.io/ECON526/lectures/lectures/latent_variables.html), [Jupyter](lectures/lectures/latent_variables.ipynb), [PDF](lectures/lectures/latent_variables.pdf) - **Self-study:** - [scikit-learn PCA docs](https://scikit-learn.org/stable/modules/decomposition.html#principal-component-analysis-pca) - [seaborn tutorials](https://seaborn.pydata.org/tutorial/introduction.html) -- **September 16**: More on Latent Variables and Clustering +- **September 18**: More on Latent Variables and Clustering - **Topics:** Finish off continuous latent variables, PCA, auto-encoders, clustering, and started dynamics - **Material:** - [Latent Variables and Unsupervised Learning](https://ubcecon.github.io/ECON526/lectures/lectures/latent_variables.html) - **Self-study:** - [scikit-learn k-means docs](https://scikit-learn.org/stable/modules/clustering.html#k-means) -- **September 18**: Dynamics +- **September 23**: Dynamics - **Topics:** Dynamical systems, stability, fixed points, linearization, intro to the Solow-Swan growth model - **Material:** - [Linear and Nonlinear Dynamics](https://ubcecon.github.io/ECON526/lectures/lectures/dynamics.html), [Jupyter](lectures/lectures/dynamics.ipynb), [PDF](lectures/lectures/dynamics.pdf) @@ -106,7 +120,7 @@ Slides for the lectures can be found [here](https://ubcecon.github.io/ECON526/le - **(Optional) Extra Material**: - More on the Solow Model and Python: https://python-programming.quantecon.org/python_oop.html#example-the-solow-growth-model -- **September 23**: Probability, Randomness, and Independence +- **September 25**: Probability, Randomness, and Independence - **Topics:** Axioms of probability, LLN and CLT, and Conditional Independence - **Material:** - [Probability, Conditioning, and Independence](https://ubcecon.github.io/ECON526/lectures/lectures/probability.html), [Jupyter](lectures/lectures/probability.ipynb), [PDF](lectures/lectures/probability.pdf) @@ -118,7 +132,9 @@ Slides for the lectures can be found [here](https://ubcecon.github.io/ECON526/le - https://python.quantecon.org/prob_meaning.html - https://python.quantecon.org/prob_matrix.html -- **September 25**: Stochastic Processes and Forecasts +- **September 30 (Statutory holiday)** + +- **October 2**: Stochastic Processes and Forecasts - **Topics:** Conditional expectations, Bayes' rule, Law of Iterated Expectations, stochastic processes - **Material:** - Finish [Probability, Conditioning, and Independence](https://ubcecon.github.io/ECON526/lectures/lectures/probability.html) @@ -128,30 +144,29 @@ Slides for the lectures can be found [here](https://ubcecon.github.io/ECON526/le - https://python.quantecon.org/ar1_processes.html for more on AR(1) processes - https://datascience.quantecon.org/scientific/randomness.html#loan-states for a simple Markov Chain example -- **September 30 (Statutory holiday)** -- **October 2**: Markov Chains and Introduction to Causality and Counterfactuals - - **Topics:** Finish stochastic processes and Markov Chains and briefly setup causality and counterfactuals +- **October 7**: Markov Chains + - **Topics:** Finish stochastic processes and Markov Chains and briefly setup causality and counterfactuals if time permits - **Material:** - Finish [Stochastic Processes](https://ubcecon.github.io/ECON526/lectures/lectures/stochastic_processes.html) - **Self-Study:** - https://matheusfacure.github.io/python-causality-handbook/01-Introduction-To-Causality.html -- **October 7** Introduction to Causality, continued +- **October 9** Introduction to Causality and Counterfactuals - **Material:** - [Introduction to Causality and Randomized Experiments](https://ubcecon.github.io/ECON526/lectures/lectures/introduction_to_causality.html) - **Self-study:** - https://matheusfacure.github.io/python-causality-handbook/01-Introduction-To-Causality.html - https://matheusfacure.github.io/python-causality-handbook/02-Randomised-Experiments.html -- **October 9** Stats Review: Quantifying Uncertainty in Causal Inference +- **October 14 (Statutory holiday)** + +- **October 16** Stats Review: Quantifying Uncertainty in Causal Inference + Midterm Logistics REview - **Material:** - [Stats Review: Quantifying Uncertainty in Applied Economics](https://ubcecon.github.io/ECON526/lectures/lectures/uncertainty_bias_variance.html) - **Self-study:** - https://matheusfacure.github.io/python-causality-handbook/03-Stats-Review-The-Most-Dangerous-Equation.html -- **October 14 (Statutory holiday)** -- **October 16 (TA session Midterm Review)** - **October 21 (IN CLASS MIDTERM)** ### Paul