| Week 1 (Aug 25) |
Course Overview |
R Programming |
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| Week 2 (Sep 1) |
R Programming (cont.) |
R Programming (cont.) |
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| Week 3 (Sep 8) |
Shell scripting |
Git(hub) |
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| Week 4 (Sep 15) |
Data wrangling and tidying |
In-Class Exam 1 |
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| HW #1 due on Monday Sep 15 |
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| Week 5 (Sep 22) |
Taming the data zoo: Spatial data and GIS |
Taming the data zoo: Text data |
Web scrapping |
| Week 6 (Sep 29) |
Sampling, bootstrap |
Linear regression and beyond |
Database and SQL |
| Week 7 (Oct 6) |
Beyond linear models |
Concepts in Machine Learning |
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| HW #2 due on Monday Oct 6 |
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| Week 8 (Oct 13) |
Proposal Presentation |
Regularized Regressions |
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| Week 9 (Oct 20) |
Trees, SVM, boostings |
Unsupervised learning |
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Written proposal due |
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| Week 10 (Oct 27) |
(Deep) neural netwoks |
Causality |
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| Week 11 (Nov 3) |
Randomized Experiments |
Rescheduled |
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| Week 12 (Nov 10) |
Causal inference with observational data |
Causal inference with observational data (cont.) |
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Causal inference powered by ML and AI |
In-Class Exam 2 |
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| Week 13 (Nov 17) |
Time Series Forecasting |
Social networks |
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| Week 14 (Nov 24) |
Numerical optimization |
LLM and AI |
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| Week 15 (Dec 1) |
Final Presentation |
Final Presentation |
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| Week 16 (Dec 8) |
Rescheduled |
No Class |
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| Final report due on Monday Dec 15 |
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