Added readmes and minor corrections

Convex/README.md

@@ -0,0 +1,4 @@
+# Convex Optimisation
+
+ Contains optimisation algorithms written from scratch. Code here is correct, but not of very high quality. I do intend to come back and
+ make it neater and faster and improve generalisability. 

High Dimensional/Latent Factor Model/Latent Factor Book Recommendation System.ipynb

@@ -1949,7 +1949,7 @@
     "id": "6cpFQr_IsMIg"
    },
    "source": [
-    "Here we see that the user reversion model gives better test mse than using book reversion, and better than our best naive model. This promising news. The User reversion to the mean is, currently, our best model. Now we move on to trying factor models, and aim to beat the mse of 2.34."
+    "Here we see that the user reversion model gives better test mse than using book reversion, and better than our best naive model. This promising news. The User reversion to the mean is, currently, our best model. Now we move on to trying factor models, and aim to beat the mse of 2.73."
    ]
   },
   {
@@ -4809,7 +4809,7 @@
     "id": "pidCHl2_L6mZ"
    },
    "source": [
-    "And here we see the merits of our best model on the test set as well: it achieves a test error of 1.95, much lower than our reversion to the mean model which was what we modelled our residuals on. This thus becomes our final model: we first use user reversion to the mean, use a factor model to model the residuals and add that to the rating to obtain our final prediction for ratings."
+    "And here we see the merits of our best model on the test set as well: it achieves a test error of 2.71, much lower than our reversion to the mean model which was what we modelled our residuals on. This thus becomes our final model: we first use user reversion to the mean, use a factor model to model the residuals and add that to the rating to obtain our final prediction for ratings."
    ]
   }
  ],
@@ -6328,4 +6328,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 1
-}
+}

High Dimensional/README.md

@@ -0,0 +1,6 @@
+# High Dimensional Optimisation
+
+ Contains jupyter notebooks that serve some supervised learning objective using optimisation algorithms written from scratch. 
+ Although I never received any feedback nor scoring for this work (the prof never delivered on his promise) 
+ I did do rather decently for this module, leading me to believe code here was probably correct.
+

README.md

@@ -1,2 +1,6 @@
 # Optimisation
- Contains optimisation algorithms written from scratch and some data analysis also using optimisation algos written from scratch.
+ Contains optimisation algorithms written from scratch and some data analysis also using optimisation algorithms written from scratch.
+
+Specifically, it contains code written for modules DSA3102 Convex Optimisation and DSA4212 High Dimensional Optimisation, and is 
+sorted as such: convex is code from 3102, and high dimensional denotes code from 4212. This repo will also contain any 
+other optimisation algorithms I write.