http://liftothers.org/dokuwiki/ 2026-07-10T19:58:40+00:00 http://liftothers.org/dokuwiki/ http://liftothers.org/dokuwiki/lib/tpl/dokuwiki/images/favicon.ico text/html 2021-06-30T23:42:05+00:00 http://liftothers.org/dokuwiki/doku.php?id=cs401r_w2016:fp&rev=1625096525&do=diff Objective: To creatively apply knowledge gained through the course of the semester to a substantial data analysis problem of your own choosing. ---------- Deliverable: For your final project, you will find a dataset and apply your data analysis skills to a new problem based on the data. You will turn in a PDF report discussing your efforts, don't include code in your report. text/html 2021-06-30T23:42:05+00:00 http://liftothers.org/dokuwiki/doku.php?id=cs401r_w2016:lab1&rev=1625096525&do=diff Objective: Get started with anaconda, python, ipython notebooks, and pandas. Begin producing simple visualizations of data and images. ---------- Deliverable: For this lab, you will submit an ipython notebook. This notebook will have two parts: text/html 2021-06-30T23:42:05+00:00 http://liftothers.org/dokuwiki/doku.php?id=cs401r_w2016:lab2&rev=1625096525&do=diff Objective: To understand the relationship between a prior, a likelihood, a posterior and the posterior predictive distribution. To understand that distributions can be placed over arbitrary objects, including things like abstract sequences of numbers. text/html 2021-06-30T23:42:05+00:00 http://liftothers.org/dokuwiki/doku.php?id=cs401r_w2016:lab3&rev=1625096525&do=diff Objective: To understand how to sample from different distributions, and to understand the link between samples and a PDF/PMF. To explore different parameter settings of common distributions, and to implement a small library of random variable types. text/html 2021-06-30T23:42:05+00:00 http://liftothers.org/dokuwiki/doku.php?id=cs401r_w2016:lab4&rev=1625096525&do=diff Objective: To understand Gaussian process regression, and to be able to generate nonparametric regressions with confidence intervals. Also to understand the interplay between a kernel (or covariance) function, and the resulting confidence intervals of the regression. text/html 2021-06-30T23:42:05+00:00 http://liftothers.org/dokuwiki/doku.php?id=cs401r_w2016:lab5&rev=1625096525&do=diff Objective: To understand how to use kernel density estimation to both generate a simple classifier and a class-conditional visualization of different hand-written digits. ---------- Deliverable: You should turn in an iPython notebook that performs three tasks. All tasks will be done using the MNIST handwritten digit data set (see Description for details): text/html 2021-06-30T23:42:05+00:00 http://liftothers.org/dokuwiki/doku.php?id=cs401r_w2016:lab7&rev=1625096525&do=diff Objective: To understand state space models, and in particular, the Kalman filter. ---------- Pre-requisite: You must install the python skimage package. This can be installed via conda install scikit-image. ---------- Deliverable: For this lab, you will implement the Kalman filter algorithm on two datasets. Your notebook should produce two visualizations of the observations, the true state, the estimated state, and your estimate of the variance of your state estimates. text/html 2021-06-30T23:42:05+00:00 http://liftothers.org/dokuwiki/doku.php?id=cs401r_w2016:lab8&rev=1625096525&do=diff Objective: To understand particle filters, and to gain experience with debugging likelihoods. ---------- Suggested pre-requisite: You may wish to install the python pyqtgraph package. This can be installed via conda install pyqtgraph. This is a plotting package that is significantly faster than text/html 2021-06-30T23:42:05+00:00 http://liftothers.org/dokuwiki/doku.php?id=cs401r_w2016:lab9&rev=1625096525&do=diff Objective: To understand the LDA model and Gibbs sampling. ---------- Deliverable: For this lab, you will implement Gibbs sampling on the LDA model. You will use as data a set of about 400 General Conference talks. Your notebook should implement two different inference algorithms on the LDA model: (1) a standard Gibbs sampler, and (2) a collapsed Gibbs sampler. text/html 2021-06-30T23:42:05+00:00 http://liftothers.org/dokuwiki/doku.php?id=cs401r_w2016:lab10&rev=1625096525&do=diff Objective: To understand MCMC and Hamiltonian MCMC. ---------- Pre-requisite: You need to install the autograd package. This can be installed with pip install autograd. ---------- Deliverable: For this lab, you will implement two variants of MCMC inference: basic Metropolis Hastings and Hamiltonian MCMC. Your notebook should present visualizations of both the resulting samples, as well as plots of the state over time. text/html 2021-06-30T23:42:05+00:00 http://liftothers.org/dokuwiki/doku.php?id=cs401r_w2016:lab12&rev=1625096525&do=diff Objective: To understand recommender systems, and to have a significant, creative experience exploring a large dataset in a competition-style setting. ---------- Deliverable: For this lab, you will construct a movie recommendation engine, using a simple publicly available dataset. For this lab, you will turn in three things: text/html 2021-06-30T23:42:05+00:00 http://liftothers.org/dokuwiki/doku.php?id=cs401r_w2016:lab13&rev=1625096525&do=diff Objective: To understand expectation maximization, and to explore how to learn the parameters of a Gaussian mixture model. ---------- Deliverable: For this lab, you will implement the Expectation Maximization algorithm on the Old Faithful dataset. This involves learning the parameters of a Gaussian mixture model. Your notebook should produce a visualization of the progress of the algorithm. The final figure could look something like this (they don't have to be arranged in subplots): text/html 2021-06-30T23:42:05+00:00 http://liftothers.org/dokuwiki/doku.php?id=cs401r_w2016:lab14&rev=1625096525&do=diff Objective: To understand how to cope with large amounts of data, and how to compositionally construct custom kernels that mix several different types of data. ---------- Pre-requisite: To complete this lab, you will need to create a Kaggle account. ---------- Deliverable: text/html 2021-06-30T23:42:05+00:00 http://liftothers.org/dokuwiki/doku.php?id=cs401r_w2016:lab_notes&rev=1625096525&do=diff Throughout this class, we'll be using several python packages extensively. If you've never used them before, we strongly encourage you to play around with them and get comfortable. They are: * numpy - Python's package that provides high-performance matrix, vector and linear algebra operations