Update objectives

Follows carpentries-incubator#3

_episodes/05-dimensionality-reduction.md

@@ -3,15 +3,17 @@ title: "Dimensionality Reduction"
 teaching: 0
 exercises: 0
 questions:
-- "How can we perform unsupervised learning with dimensionality reduction techniques such as PCA and TSNE?"
+- "How can we perform unsupervised learning with dimensionality reduction techniques such as Principle Component Analyis (PCA), 
+Uniform Manifold Approximation and Projection  (UMAP) and t-distributed Stochastic Neighbor Embedding (t-SNE)?"
 objectives:
 - "Recall that most data is inherently multidimensional"
 - "Understand that reducing the number of dimensions can simplify modelling and allow classifications to be performed."
-- "Recall that PCA is a popular technique for dimensionality reduction."
-- "Recall that TSNE is another technique for dimensionality reduction."
-- "Apply PCA and TSNE with Scikit Learn to an example dataset."
-- "Evaluate the relative peformance of PCA and TSNE."
+- "Understand that dimensionality reduction is helpful in performing data visualization and interpretation"
+- "Apply PCA, t-SNE and UMAP with Scikit Learn to an example dataset."
+- "Evaluate the relative peformance of PCA, t-SNE and UMAP."
 keypoints:
-- "PCA is a dimensionality reduction technique"
-- "TSNE is another dimensionality reduction technique"
+- "PCA is a linear dimensionality reduction technique"
+- "t-SNE is another dimensionality reduction technique that is more general than PCA"
+- "UMAP is another dimensionality reduction technique that allows for nonlinear embeddings"
+
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