- Introduction to Python
- Basic data types, mutable/immutable objects
- Data structures: lists, dictionaries, unordered sets, tuples
- Control flow, list comprehensions, functions
- Numpy, Pandas, Matplotlib
- Vectors, matrices, linear algebra
- How do I make my code run faster? – vectorized functions
- Data frames, I/O, visualization
- Scikit Learn
- Linear regression
- Logistic regression
- Random forests, boosting
- Cross validation, model selection
- Optimization with Pyomo
- Writing and solving an optimization program
- Machine learning with Pyomo, robust regression, sparse regression
- Please navigate to https://www.anaconda.com/download/ and download Python 3.7 version of Anaconda.
- After installation, open Anaconda and launch Jupyter notebook 5.0.0 or above.
- Either a browser should open automatically, or a terminal will open. Follow the instructions on the terminal.
- If you see a Jupyter screen in your browser, you have completed the pre-assignment!
- Navigate to https://www.coin-or.org/Ipopt/documentation/ and download and install Ipopt.
- Make sure gurobi is set up on your computer and that your academic license is valid.
Please email me at oskali@mit.edu if you have any questions.
Omar
Credits to Phil Chodrow