diff --git a/syllabus.md b/syllabus.md
index a562168..3fa6648 100644
--- a/syllabus.md
+++ b/syllabus.md
@@ -74,11 +74,11 @@ There will not be enough time in the workshop to cover all of `macpan2`, so ther
 
 Participants will learn how to do the following types of tasks required for exploring model simulations.
 
-- [ ]  Find candidate simulation models in the `macpan2` [library](#library-models) of starter models.
-- [ ] Prepare certain [types of data](#types-of-data) so that they can be compared with `macpan2` simulation output, both visually and numerically.
-- [ ] Make [modifications to models](#model-modification-tools) in the library.
-- [ ] Compute [epidemiological summaries](#epidemiological-model-summaries) (e.g., basic reproduction number, $\mathcal{R}_0$).
-- [ ] Cast a model as a particular [dynamical model type](#dynamical-model-types) (e.g. discrete-time recursion, ordinary differential equation).
+* Find candidate simulation models in the `macpan2` [library](#library-models) of starter models.
+* Prepare certain [types of data](#types-of-data) so that they can be compared with `macpan2` simulation output, both visually and numerically.
+* Make [modifications to models](#model-modification-tools) in the library.
+* Compute [epidemiological summaries](#epidemiological-model-summaries) (e.g., basic reproduction number, $\mathcal{R}_0$).
+* Cast a model as a particular [dynamical model type](#dynamical-model-types) (e.g. discrete-time recursion, ordinary differential equation).
 
 
 <!-- omit from toc -->
@@ -86,11 +86,11 @@ Participants will learn how to do the following types of tasks required for expl
 
 Participants will learn how to do the following types of tasks required for parameterizing models for making inferences about a particular population and public health problem.
 
-- [ ] Modify the default values of parameters in a model.
-- [ ] Express uncertainty in model parameters (e.g., transmission rate) or in [epidemiological summaries](#epidemiological-model-summaries) (e.g., $\mathcal{R}_0$) with prior distributions.
-- [ ] Use [optimization](#optimization) to calibrate parameters (e.g., transmission rate) so that the discrepancy between observed and simulated data is minimized.
-- [ ] Parameterize the [initial values of the state variables](#calibrate-initial-state-variables) (e.g. `S`, `I`) so that they can be optimized.
-- [ ] Calibrate the functional form of time-variation of parameters using machine learning components embedded within epidemiological models. This is a useful technique when the reasons for parameter time-variation are not well-understood.
+* Modify the default values of parameters in a model.
+* Express uncertainty in model parameters (e.g., transmission rate) or in [epidemiological summaries](#epidemiological-model-summaries) (e.g., $\mathcal{R}_0$) with prior distributions.
+* Use [optimization](#optimization) to calibrate parameters (e.g., transmission rate) so that the discrepancy between observed and simulated data is minimized.
+* Parameterize the [initial values of the state variables](#calibrate-initial-state-variables) (e.g. `S`, `I`) so that they can be optimized.
+* Calibrate the functional form of time-variation of parameters using machine learning components embedded within epidemiological models. This is a useful technique when the reasons for parameter time-variation are not well-understood.
 
 
 
@@ -99,12 +99,12 @@ Participants will learn how to do the following types of tasks required for para
 
 Participants will learn how to make the following types of inferences using realistically parameterized models.
 
-- [ ] Visualize goodness-of-fit.
-- [ ] Generate confidence intervals for estimated parameters.
-- [ ] Forecast model variables beyond the last data point.
-- [ ] Calculate prediction intervals measuring uncertainty about these forecasts.
-- [ ] Compare alternative scenarios for counter-factual causal analysis (e.g., how many deaths were saved due to vaccination?).
-- [ ] Produce uncertainty estimates for [epidemiological model summaries](#epidemiological-model-summaries) like $\mathcal{R}_0$.
+* Visualize goodness-of-fit.
+* Generate confidence intervals for estimated parameters.
+* Forecast model variables beyond the last data point.
+* Calculate prediction intervals measuring uncertainty about these forecasts.
+* Compare alternative scenarios for counter-factual causal analysis (e.g., how many deaths were saved due to vaccination?).
+* Produce uncertainty estimates for [epidemiological model summaries](#epidemiological-model-summaries) like $\mathcal{R}_0$.
 
 
 <!-- omit from toc -->
@@ -112,11 +112,11 @@ Participants will learn how to make the following types of inferences using real
 
 Participants will learn the following stratification strategies.
 
-- [ ] [Stratify every compartment](#cartesian-product-models) in the same way (e.g. by age, location).
-- [ ] [Stratify infectious compartments](#stratify-infectious-compartments) (e.g. by symptom status).
-- [ ] Expand a single compartment into a sequential chain, to model different distributions of time spent in that compartment.
-- [ ] Combine several single-strain models into a single multi-strain model.
-- [ ] Stratify compartments based on immunity status. This is particularly useful in cases where immunity is partial, can wane, and is caused by a mixture of past infection, vaccination, and cross-immunity.
+* [Stratify every compartment](#cartesian-product-models) in the same way (e.g. by age, location).
+* [Stratify infectious compartments](#stratify-infectious-compartments) (e.g. by symptom status).
+* Expand a single compartment into a sequential chain, to model different distributions of time spent in that compartment.
+* Combine several single-strain models into a single multi-strain model.
+* Stratify compartments based on immunity status. This is particularly useful in cases where immunity is partial, can wane, and is caused by a mixture of past infection, vaccination, and cross-immunity.
 
 
 ## Materials