Resources | Statistical Pattern Recognition

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Python Setup

For detailed instructions, see the [Python Setup Instructions page]

Starter code is available in our public Github repository: https://github.com/tufts-ml-courses/cs136-24s-assignments

Python Resources

To gain some fundamental Python skills (assuming you know other programming), we recommend:

Whirlwind Tour of Python by Jake van der Plas

Jupyter Notebooks

For many in-class breakout sessions, you'll want to work through a provided notebook, distributed as a `.ipynb' file from our course starter code repository on Github.

You'll want to download this file and run it on your machine.

To launch a specific notebook file named MyNotebook.ipynb, here's what you'll do in your Terminal (Linux/Mac) or Command Prompt (Windows):

# Before we can start, be sure your current directory contains `MyNotebook.ipynb`

# First, activate our course conda environment
$ conda activate spr_2024s_env

# Second, launch the notebook server and direct it to open `MyNotebook.ipynb`
$ jupyter notebook MyNotebook.ipynb

# Should automatically open a browser and take you to an interactive notebook session. Or click `localhost:8888` link below.

For more help on launching a notebook, see Jupyter notebook documentation

Jupyter Resources

If you don't know much about Jupyter, the resources below might be helpful

How to download a Jupyter notebook and open it in your browser

Windows instructions

'Play with Data in Jupyter' lessons by Lorena Barba

Self-Study Resources

Here are some useful resources to help you catch up if you are missing some of the pre-requisite knowledge. Please contribute new resources by starting a topic on the class discussion forum.

Probability

Key concepts:
- Sum rule and product rule of probability
- Bayes theorem and associated algebra
- Continuous and discrete random variables
Litmus test:
- Do the lecture notes from day01.pdf seem familiar? Do you have enough prevous experience and math sophistication to follow these easily?
Possible resources:
- Probability review notes from Prof. David Blei: http://www.cs.columbia.edu/~blei/fogm/2016F/doc/probability_review.pdf
- Stanford CS229 notes on Gaussian distributions: http://cs229.stanford.edu/section/gaussians.pdf

First-order gradient-based optimization

Key concepts:
- Gradient descent
- Learning rates
- Difference between convex and non-convex functions for minimization
Litmus test:
- Could you fit a linear regression model via gradient descent? (see notebook below).
Possible resources:
- Convex Optimization overview for Stanford CS229: http://cs229.stanford.edu/section/cs229-cvxopt.pdf
- Jupyter notebook on 'Linear Regression with NumPy' (fits linear model with gradient descent): https://www.cs.toronto.edu/~frossard/post/linear_regression/