├── .gitignore ├── .gitpod.Dockerfile ├── .gitpod.yml ├── Assignment.md ├── Dockerfile ├── LICENSE ├── README.md ├── TODO.md ├── code ├── Colored_neural_network.png ├── MLE_normal.R ├── MLE_normal.py ├── lec1_LLN_CLT.ipynb ├── lec2_inid.ipynb ├── lec2_logit_py.ipynb ├── lec3_empirical_process.ipynb ├── lianjia.RData ├── nn_Poisson_Keras_HD.ipynb ├── nn_torch.ipynb ├── partial_sum.R └── shinny_newton_method_opt.R ├── lec_notes_2024 ├── 07-optimization.ipynb ├── 08_time_series.ipynb ├── 09-ML.ipynb ├── 10-ML2.ipynb ├── 5150.bib ├── book_style.lyx ├── book_style_2023.pdf ├── lec0.md ├── lec1-asym.lyx ├── lec1-asym.pdf ├── lec11-nonstationary_times_series.lyx ├── lec11-nonstationary_times_series.pdf ├── lec2-inid.lyx ├── lec2-inid.pdf ├── lec3-emp-process.lyx ├── lec3-emp-process.pdf ├── lec4-m-estimator.lyx ├── lec4-m-estimator.pdf ├── lec5-gmm.lyx ├── lec5-gmm.pdf ├── lec5-mle.lyx ├── lec5-mle.pdf ├── lec6-quantile.lyx ├── lec6-quantile.pdf ├── lec8-time-series.lyx ├── lec8-time-series.pdf ├── lec9-arma.lyx └── lec9-arma.pdf ├── lec_notes_2025 ├── 00_intro.ipynb ├── 01_potential_outcomes.ipynb ├── 02_regression.lyx ├── 02_regression.pdf ├── 03_OLS.lyx ├── 03_OLS.pdf ├── 04_asym.lyx ├── 04_asym.pdf ├── 04_asym_supp.lyx ├── 04_asym_supp.pdf ├── 06-MLE_slides.pdf ├── 06-mle.pdf ├── 06_mle.lyx ├── 07-MLE_slides2.pdf ├── 07-m-estimator.pdf ├── 07_m-estimator.lyx ├── 08_optimization.ipynb ├── 09_computing.ipynb ├── 10_gmm.lyx ├── 10_gmm.pdf ├── 11_time-series.lyx ├── 11_time-series.pdf ├── 11_time_series_nonstationary.ipynb ├── 12_ML.lyx ├── 12_ML.pdf ├── 12_ML_demo.ipynb ├── 12_ML_demo2.ipynb ├── 13_ML2.lyx ├── 13_ML2.pdf ├── 13_ML2_NN.ipynb ├── 14_NN.ipynb ├── 14_OLS_NN.ipynb ├── 14_logit_NN.ipynb ├── 5150.bib └── code_demo │ ├── 03_OLS_py.ipynb │ ├── 04_asym_py.ipynb │ ├── 07-logistic.ipynb │ ├── 08_newton_method_opt.R │ ├── 09_loop.py │ ├── 09_loop.r │ └── 09_parallel.py ├── lec_notes_2026 ├── 00_intro.ipynb ├── 01_potential_outcomes.ipynb ├── 02_regression.lyx ├── 02_regression.pdf ├── 03_OLS.lyx ├── 03_OLS.pdf ├── 04_asym.lyx ├── 04_asym.pdf ├── 04_asym_supp.lyx ├── 04_asym_supp.pdf ├── 06-MLE_slides.pdf ├── 06-mle.pdf ├── 06_mle.lyx ├── 07-MLE_slides2.pdf ├── 07-m-estimator.pdf ├── 07_m-estimator.lyx ├── 08_optimization.ipynb ├── 09_computing.ipynb ├── 09_computing_cp.ipynb ├── 10_gmm.lyx ├── 10_gmm.pdf ├── 11_time-series.lyx ├── 11_time-series.pdf ├── 11_time_series_nonstationary.ipynb ├── 12_ML.lyx ├── 12_ML.pdf ├── 12_ML_demo.ipynb ├── 12_ML_demo2.ipynb ├── 13_ML2.lyx ├── 13_ML2.pdf ├── 13_ML2_NN.ipynb ├── 14_NN.ipynb ├── 14_OLS_NN.ipynb ├── 14_logit_NN.ipynb ├── 5150.bib └── code_demo │ ├── 03_OLS_py.ipynb │ ├── 04_asym_py.ipynb │ ├── 07-logistic.ipynb │ ├── 08_newton_method_opt.R │ ├── 09_loop.py │ ├── 09_loop.r │ └── 09_parallel.py └── references ├── Donoho 2023 AI singularity.pdf ├── Heckman Pinto 2023 causality.pdf └── Stigler 2007 epic story of maximum likelihood.pdf /.gitignore: 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