typos

doc/src/week48/week48.do.txt

@@ -38,13 +38,14 @@ o Readings and Videos:
 The algorithm described here can be applied to both classification and regression problems.
 
 We will grow of forest of say $B$ trees.
-o For $b=1:B$
+* For $b=1:B$
   o Draw a bootstrap sample from the training data organized in our $\bm{X}$ matrix.
   o We grow then a random forest tree $T_b$ based on the bootstrapped data by repeating the steps outlined till we reach the maximum node size is reached
     o we select $m \le p$ variables at random from the $p$ predictors/features
     o pick the best split point among the $m$ features using for example the CART algorithm and create a new node
     o split the node into daughter nodes
-o Output then the ensemble of trees $\{T_b\}_1^{B}$ and make predictions for either a regression type of problem or a classification type of problem. 
+
+Finally we output then the ensemble of trees $\{T_b\}_1^{B}$ and make predictions for either a regression type of problem or a classification type of problem. 
 
 
 
@@ -925,22 +926,21 @@ o Not discussed: Principal Component Analysis to reduce the number of features.
 !split
 ===== Machine learning =====
 
-The following topics will be covered
-o Linear methods for regression and classification:
+* Linear methods for regression and classification:
   o Ordinary Least Squares
   o Ridge regression
   o Lasso regression
   o Logistic regression
-o Neural networks and deep learning:
+* Neural networks and deep learning:
   o Feed Forward Neural Networks
   o Convolutional Neural Networks
   o Recurrent Neural Networks
-o Decisions trees and ensemble methods:
+* Decisions trees and ensemble methods:
   o Decision trees
   o Bagging and voting
   o Random forests
   o Boosting and gradient boosting
-o Not discussed this year: Support vector machines
+* Not discussed this year: Support vector machines
   o Binary classification and multiclass classification
   o Kernel methods
   o Regression
@@ -1011,43 +1011,41 @@ o Based on your results, feedback loop to earliest possible point
 !split
 ===== Choose a Model and Algorithm  =====
 
-o Supervised?
-o Start with the simplest model that fits your problem
-o Start with minimal processing of data
+* Supervised?
+* Start with the simplest model that fits your problem
+* Start with minimal processing of data
 
 !split
 ===== Preparing Your Data =====
 
-o Shuffle your data
-o Mean center your data
+* Shuffle your data
+* Mean center your data
   * Why?
-o Normalize the variance
+* Normalize the variance
   * Why?
-o _Whitening_
+* _Whitening_
   * Decorrelates data
   * Can be hit or miss
-o  When to do train/test split?
+*  When to do train/test split?
 
 
 !split
 =====  Which activation and weights to choose in neural networks =====
 
-o RELU? ELU? GELU? etc
-o Sigmoid or Tanh?
-o Set all weights to 0?
-  * Terrible idea
-o Set all weights to random values?
-  * Small random values
+* RELU? ELU? GELU? etc
+* Sigmoid or Tanh?
+* Set all weights to 0? Terrible idea
+* Set all weights to random values? Small random values
 
 
 !split
 ===== Optimization Methods and Hyperparameters =====
-o Stochastic gradient descent
-o  Stochastic gradient descent + momentum
-o State-of-the-art approaches:
-  * RMSProp
-  * Adam
-  * and more
+* Stochastic gradient descent
+*  Stochastic gradient descent + momentum
+* State-of-the-art approaches:
+  o RMSProp
+  o Adam
+  o and more
 
 Which regularization and hyperparameters? $L_1$ or $L_2$, soft
 classifiers, depths of trees and many other. Need to explore a large