Deploying to Fall24-output from @ 4f8f6e2 🚀

Fall24/cheatsheets/index.html

@@ -95,7 +95,7 @@ <h3>Module 1: Images, Transformations, Abstractions</h3>
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 <h3>Module 2: Social Science &amp; Data Science</h3>
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-<li class=' lecture tag_lecture tag_module2 tag_track_data tag_data tag_statistics tag_matrix tag_linear_algebra tag_track_math'><a href='../data_science/pca/' title='In this notebook we will start looking at more general kinds of data, not only images, and we&apos;ll try to extract some information from the image using statistical methods, namely principal component analysis.  This method tries to answer the questions &quot;which &apos;directions&apos; are the most important in the data&quot; and &quot;can we reduce the dimensionality (number of useful variables) of the data&quot;?'><span class="entry-number">2.1</span> Principal Component Analysis</a></li><li class=' lecture tag_lecture tag_module2 tag_track_data tag_track_math tag_random tag_statistics tag_track_julia tag_image tag_probability tag_plotting tag_interactive'><a href='../data_science/random_vars/' title=''><span class="entry-number">2.2</span> Sampling and Random Variables</a></li><li class=' homework tag_homework tag_module2 tag_track_julia tag_structure tag_track_math tag_type tag_matrix tag_linear_algebra tag_track_data'><a href='../homework/hw5/' title='Create your own Julia structs and add new functionality to them, to create first-class mathematical objects.'><span class="entry-number">Homework 5:</span> Structure</a></li><li class=' lecture tag_lecture tag_module2 tag_track_julia tag_probability tag_statistics tag_track_math tag_epidemiology tag_interactive tag_plotting tag_programming tag_type tag_discrete tag_continuous tag_ODE tag_differential_equation tag_agent_based_model'><a href='../data_science/simulating_component_failure/' title=''><span class="entry-number">2.3</span> Modeling with Stochastic Simulation</a></li><li class=' homework tag_homework tag_module2 tag_track_julia tag_track_math tag_track_data tag_structure tag_probability tag_statistics tag_plotting tag_interactive'><a href='../homework/hw6/' title='Calculate a probability distribution from a dataset, experiment with different statistical models, and learn how to plot your results.'><span class="entry-number">Homework 6:</span> Probability distributions</a></li><li class=' lecture tag_lecture tag_module2 tag_track_julia tag_type tag_programming tag_probability tag_interactive tag_random tag_track_math tag_Symbolics'><a href='../data_science/random_variables_as_types/' title=''><span class="entry-number">2.4</span> Random Variables as Types</a></li><li class=' lecture tag_lecture tag_module2 tag_programming tag_track_julia tag_plotting tag_structure tag_type tag_interactive tag_random tag_statistics tag_track_math tag_track_data'><a href='../data_science/random_walks backup 1/' title=''><span class="entry-number">2.5</span> Random Walks</a></li><li class=' lecture tag_lecture tag_module2 tag_programming tag_track_julia tag_plotting tag_structure tag_type tag_interactive tag_random tag_statistics tag_track_math tag_track_data'><a href='../data_science/random_walks/' title=''><span class="entry-number">2.5</span> Random Walks</a></li><li class=' lecture tag_lecture tag_module2 tag_track_julia tag_track_data tag_statistics tag_plotting tag_random tag_structure tag_type tag_programming tag_interactive'><a href='../data_science/random_walks_II/' title=''><span class="entry-number">2.6</span> Random Walks II</a></li><li class=' lecture tag_lecture tag_module2 tag_track_math tag_discrete tag_continuous'><a href='../data_science/discrete_and_continuous/' title=''><span class="entry-number">2.7</span> Discrete and Continuous</a></li><li class=' homework tag_homework tag_module2 tag_epidemiology tag_track_data tag_monte_carlo tag_statistics tag_track_math tag_ODE tag_agent_based_model tag_differential_equation tag_type tag_structure tag_plotting'><a href='../homework/hw7/' title='Simulate the spread of an epidemic by creating your own agent-based model from scratch, and find statistics using the Monte Carlo method.'><span class="entry-number">Homework 7:</span> Epidemic modeling I</a></li><li class=' lecture tag_lecture tag_module2 tag_track_julia tag_track_data tag_csv tag_dataframe tag_statistics tag_plotting tag_interactive'><a href='../data_science/linearmodel_datascience/' title=''><span class="entry-number">2.8</span> Linear Model, Data Science, &amp; Simulations</a></li><li class=' lecture tag_lecture tag_module2 tag_track_juli tag_track_climate tag_track_data tag_track_math tag_optimization'><a href='../data_science/optimization/' title='We use Optim.jl and JuMP.jl to optimize a function: automatically finding the input that maximizes output.'><span class="entry-number">2.9</span> Optimization</a></li><li class=' homework tag_homework tag_module2 tag_track_math tag_track_data tag_optimization tag_statistics tag_gradient tag_differentiation tag_automatic_differentiation tag_continuous tag_probability tag_epidemiology tag_monte_carlo tag_modeling tag_plotting'><a href='../homework/hw8/' title='Learn about optimisation and gradient descent with help from our visuals and automatic checks. We use these new skill to fit parameters of an epidemic model to match reality.'><span class="entry-number">Homework 8:</span> Epidemic modeling II</a></li>
+<li class=' lecture tag_lecture tag_module2 tag_track_data tag_data tag_statistics tag_matrix tag_linear_algebra tag_track_math'><a href='../data_science/pca/' title='In this notebook we will start looking at more general kinds of data, not only images, and we&apos;ll try to extract some information from the image using statistical methods, namely principal component analysis.  This method tries to answer the questions &quot;which &apos;directions&apos; are the most important in the data&quot; and &quot;can we reduce the dimensionality (number of useful variables) of the data&quot;?'><span class="entry-number">2.1</span> Principal Component Analysis</a></li><li class=' lecture tag_lecture tag_module2 tag_track_data tag_track_math tag_random tag_statistics tag_track_julia tag_image tag_probability tag_plotting tag_interactive'><a href='../data_science/random_vars/' title=''><span class="entry-number">2.2</span> Sampling and Random Variables</a></li><li class=' homework tag_homework tag_module2 tag_track_julia tag_structure tag_track_math tag_type tag_matrix tag_linear_algebra tag_track_data'><a href='../homework/hw5/' title='Create your own Julia structs and add new functionality to them, to create first-class mathematical objects.'><span class="entry-number">Homework 5:</span> Structure</a></li><li class=' lecture tag_lecture tag_module2 tag_track_julia tag_probability tag_statistics tag_track_math tag_epidemiology tag_interactive tag_plotting tag_programming tag_type tag_discrete tag_continuous tag_ODE tag_differential_equation tag_agent_based_model'><a href='../data_science/simulating_component_failure/' title=''><span class="entry-number">2.3</span> Modeling with Stochastic Simulation</a></li><li class=' homework tag_homework tag_module2 tag_track_julia tag_track_math tag_track_data tag_structure tag_probability tag_statistics tag_plotting tag_interactive'><a href='../homework/hw6/' title='Calculate a probability distribution from a dataset, experiment with different statistical models, and learn how to plot your results.'><span class="entry-number">Homework 6:</span> Probability distributions</a></li><li class=' lecture tag_lecture tag_module2 tag_track_julia tag_type tag_programming tag_probability tag_interactive tag_random tag_track_math tag_Symbolics'><a href='../data_science/random_variables_as_types/' title=''><span class="entry-number">2.4</span> Random Variables as Types</a></li><li class=' lecture tag_lecture tag_module2 tag_programming tag_track_julia tag_plotting tag_structure tag_type tag_interactive tag_random tag_statistics tag_track_math tag_track_data'><a href='../data_science/random_walks/' title=''><span class="entry-number">2.5</span> Random Walks</a></li><li class=' lecture tag_lecture tag_module2 tag_track_julia tag_track_data tag_statistics tag_plotting tag_random tag_structure tag_type tag_programming tag_interactive'><a href='../data_science/random_walks_II/' title=''><span class="entry-number">2.6</span> Random Walks II</a></li><li class=' lecture tag_lecture tag_module2 tag_track_math tag_discrete tag_continuous'><a href='../data_science/discrete_and_continuous/' title=''><span class="entry-number">2.7</span> Discrete and Continuous</a></li><li class=' homework tag_homework tag_module2 tag_epidemiology tag_track_data tag_monte_carlo tag_statistics tag_track_math tag_ODE tag_agent_based_model tag_differential_equation tag_type tag_structure tag_plotting'><a href='../homework/hw7/' title='Simulate the spread of an epidemic by creating your own agent-based model from scratch, and find statistics using the Monte Carlo method.'><span class="entry-number">Homework 7:</span> Epidemic modeling I</a></li><li class=' lecture tag_lecture tag_module2 tag_track_julia tag_track_data tag_csv tag_dataframe tag_statistics tag_plotting tag_interactive'><a href='../data_science/linearmodel_datascience/' title=''><span class="entry-number">2.8</span> Linear Model, Data Science, &amp; Simulations</a></li><li class=' lecture tag_lecture tag_module2 tag_track_juli tag_track_climate tag_track_data tag_track_math tag_optimization'><a href='../data_science/optimization/' title='We use Optim.jl and JuMP.jl to optimize a function: automatically finding the input that maximizes output.'><span class="entry-number">2.9</span> Optimization</a></li><li class=' homework tag_homework tag_module2 tag_track_math tag_track_data tag_optimization tag_statistics tag_gradient tag_differentiation tag_automatic_differentiation tag_continuous tag_probability tag_epidemiology tag_monte_carlo tag_modeling tag_plotting'><a href='../homework/hw8/' title='Learn about optimisation and gradient descent with help from our visuals and automatic checks. We use these new skill to fit parameters of an epidemic model to match reality.'><span class="entry-number">Homework 8:</span> Epidemic modeling II</a></li>
 </ul>
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