diff --git a/docs/make.jl b/docs/make.jl
index 67fdfd181cb..54f353c66f3 100644
--- a/docs/make.jl
+++ b/docs/make.jl
@@ -317,6 +317,7 @@ const _PAGES = [
             "tutorials/nonlinear/nested_problems.md",
             "tutorials/nonlinear/querying_hessians.md",
             "tutorials/nonlinear/complementarity.md",
+            "tutorials/nonlinear/classifiers.md",
         ],
         "Conic programs" => [
             "tutorials/conic/introduction.md",
diff --git a/docs/src/tutorials/nonlinear/classifiers.jl b/docs/src/tutorials/nonlinear/classifiers.jl
new file mode 100644
index 00000000000..1f2728e593a
--- /dev/null
+++ b/docs/src/tutorials/nonlinear/classifiers.jl
@@ -0,0 +1,185 @@
+# Copyright 2023 James Foster and contributors                                  #src
+# This Source Code Form is subject to the terms of the Mozilla Public License   #src
+# v.2.0. If a copy of the MPL was not distributed with this file, You can       #src
+# obtain one at https://mozilla.org/MPL/2.0/.                                   #src
+
+# # Classifiers
+
+# Classification problems deal with constructing functions, called *classifiers*,
+# that can efficiently classify data into two or more distinct sets. 
+# A common application is classifying previously unseen data points
+# after training a classifier on known data.
+
+# The theory and models in this tutorial come from 
+# Section 9.4 "Classification Problems" of the book 
+# [*Linear Programming with MATLAB*](https://doi.org/10.1137/1.9780898718775) (2007)
+# by M. C. Ferris, O. L. Mangasarian, and S. J. Wright.
+
+# ## Required packages
+
+# This tutorial uses the following packages
+
+using JuMP
+import DataFrames
+import HiGHS
+import Ipopt
+import LinearAlgebra
+import Plots
+import Random
+import SparseArrays
+import Test #src
+
+# ## Data and visualisation
+
+# To start, let's generate some points to test with.
+# The argument ``m`` is the number of 2-dimensional points:
+
+function generate_test_points(m; random_seed = 1)
+    rng = Random.MersenneTwister(random_seed)
+    return 2.0 .* rand(rng, Float64, m, 2)
+end
+
+# For the sake of the example, let's take ``m = 100``:
+P = generate_test_points(100);
+
+# Note that the points are represented row-wise in the generated array.
+# Let's visualise the points using the `Plots` package:
+
+r = 1.01 * maximum(abs.(P))
+plot1 = Plots.scatter(
+    P[:, 1],
+    P[:, 2];
+    xlim = (0, r),
+    ylim = (0, r),
+    label = nothing,
+    color = :white,
+    shape = :circle,
+    size = (600, 600),
+    legend = false,
+)
+
+# We want to split the points into two distinct sets on either side of a dividing line.
+# We'll then label each point depending on which side of the line it happens to fall.
+# Based on the labels of the point, we'll show how to create a classifier using a JuMP model.
+# We can then test how well our classifier reproduces the original labels and the boundary between them.
+
+# Let's make a line to divide the point into two sets by defining a gradient and constant:
+w0 = [5, 3];
+g0 = 8;
+line(v::AbstractArray; w = w0, g = g0) = LinearAlgebra.dot(w, v) - g
+line(x::Real; w = w0, g = g0) = -(w[1] * x - g) / w[2];
+
+# Julia's multiple dispatch feature allows us to define the vector and single-variable form
+# of the `line` function under the same name.
+
+# Let's add this to the plot:
+
+Plots.plot!(
+    plot1,
+    x -> line(x),
+    0.0:0.01:2.0;
+    seriestype = :line,
+    linewidth = 5,
+    lineopacity = 0.6,
+    c = :darkcyan,
+)
+
+# Now we label the points relative to which side of the line they are.
+P_pos = hcat(filter(v -> line(v) > 0, eachrow(P))...)'
+P_neg = hcat(filter(v -> line(v) < 0, eachrow(P))...)'
+
+@assert size(P_pos, 1) + size(P_neg, 1) == size(P, 1) #src
+
+Plots.scatter!(
+    P_pos[:, 1],
+    P_pos[:, 2];
+    shape = :cross,
+    markercolor = :blue,
+    markersize = 8,
+);
+Plots.scatter!(
+    P_neg[:, 1],
+    P_neg[:, 2];
+    shape = :xcross,
+    markercolor = :crimson,
+    markersize = 8,
+);
+plot1
+
+# The goal is to show we can reconstruct the line from *just* the points and labels.
+
+# ## Formulation: linear support vector machine
+
+# Firstly, we will put the point set back together row-wise as a matrix, with the labelled points group together:
+
+A = [P_pos; P_neg];
+m, n = size(A)
+
+# To keep track of the labels, we'll use a diagonal matrix where entry ``i`` of the diagonal is the
+# label for point ``i`` (row ``i`` of the matrix).
+
+D = LinearAlgebra.Diagonal([ones(size(P_pos, 1)); -ones(size(P_neg, 1))])
+
+# It is numerically useful to have the labels +1 for `S_pos`  and -1 for `S_neg`.
+
+# A classifier known as the linear *support vector machine* looks for the line function data
+# ``w`` and ``g`` such that the function ``L(v) = w^T v - g`` satisfies 
+# ``L(p) < 0`` for a point ``p`` in `S_neg` and ``L(p) > 0`` on `S_pos`. 
+# The linearly constrained quadratic program that implements this as follows:
+
+# ```math
+# \begin{aligned}
+# & \min_{w \in \mathbb{R}^n, \; g \in \mathbb{R}, \; y \in \mathbb{R}^m} & \frac{1}{2} w^T w + C \; \sum_{i=1}^m y_i \\
+# & \text{subject to} & D_{ii}( A_{i :} w - g ) + y_i & \geq 1, & i = 1 \ldots m \\
+# \end{aligned}
+# ```
+
+# We need a value for the positive penalty parameter ``C``:
+C = 1e3;
+
+# ## JuMP formulation
+
+# Now let's build and the JuMP model. We'll get ``w`` and ``g`` from the optimal solution after the
+# solve.
+
+m, n = size(A)
+
+model = Model(Ipopt.Optimizer)
+# set_silent(model)
+@variable(model, w[1:n])
+@variable(model, g)
+@variable(model, y[1:m] >= 0)
+@constraint(model, [i in 1:m], D[i, i] * (A[i, :]' * w - g) + y[i] >= 1)
+@objective(model, Min, 0.5 * w' * w + C * sum(y))
+optimize!(model)
+Test.@test termination_status(model) == LOCALLY_SOLVED    #src
+Test.@test primal_status(model) == FEASIBLE_POINT  #src
+solution_summary(model)
+
+# ## Results
+
+# We recover the solution values
+w_sol = value.(w)
+g_sol = value(g)
+y_sol = value.(y);
+println("Minimum slack: ", minimum(y_sol), "\nMaximum slack: ", maximum(y_sol))
+
+# With the solution, we can ask: was the value of the penalty constant "sufficiently large" 
+# for this data set? This can be judged in part by the range of the slack variables.
+
+# Let's add this to the plot as well and check how we did:
+
+Plots.plot!(
+    plot1,
+    x -> line(x; w = w_sol, g = g_sol),
+    0.0:0.01:2.0;
+    seriestype = :line,
+    linewidth = 5,
+    linestyle = :dashdotdot,
+    lineopacity = 0.9,
+    c = :darkblue,
+);
+plot1
+
+# We find that we have recovered the dividing line from just the information of the points
+# and their labels.
diff --git a/docs/styles/Vocab/JuMP-Vocab/accept.txt b/docs/styles/Vocab/JuMP-Vocab/accept.txt
index d188dcc1ea1..c91bde97415 100644
--- a/docs/styles/Vocab/JuMP-Vocab/accept.txt
+++ b/docs/styles/Vocab/JuMP-Vocab/accept.txt
@@ -205,6 +205,7 @@ Linial
 Luangkesorn
 Lubin
 Madani
+Mangasarian
 Marek
 Markowitz
 Mathieu