Update 2024_ML_emasters.md

stuff/2024_ML_emasters.md

@@ -30,14 +30,17 @@ There are no official pre-requisites for the course. But you may brush up your k
 |  	|  	| 6.    Linear Classification: multi-class classification, multi-label classification 	|  	|
 | 3 	| Supervised Learning 	| 7.    Neural Networks for Regression and Classification: weights, non-linearities, loss functions, training 	| 4 	|
 |  	|  	| 8.    Neural Network optimization: gradient descent methods, risk minimization, hyperparameter tuning, regularization, 	|  	|
-| 4 	| Unsupervised learning 	| 9.    Clustering: K-means 	| 3 	|
-|  	|  	| 10.    Distribution learning: Maximum likelihood estimation, Gaussian pdf, Gaussian Mixture Models 	|  	|
-|  	|  	| 11.    Dimensionality reduction and visualization: Principle component analysis 	|  	|
-| 5 	| Time series processing 	| 12.  Time series analysis 	| 2 	|
-|  	|  	| 13.  Dynamic time warping 	|  	|
-| 6 	| ML at scale 	| 14.  Parameter tuning 	| 1 	|
-|  	|  	| 15.  Model selection 	|  	|
-|  	|  	| 16.  Validation and testing 	|  	|
+|  	|  	| 9.    Convolutional NNs and Recurrent NNs 	|  	|
+| 4 	| Unsupervised learning 	| 10.    Clustering: K-means 	| 3 	|
+|  	|  	| 11.    Distribution learning: Maximum likelihood estimation, Gaussian pdf, Gaussian Mixture Models 	|  	|
+|  	|  	| 12.    Dimensionality reduction and visualization: Principle component analysis 	|  	|
+| 5 	| Time series processing 	| 13.  Time series analysis 	| 2 	|
+|  	|  	| 14.  Dynamic time warping 	|  	|
+| 6 	| ML at scale 	| 15.  Parameter tuning 	| 1 	|
+|  	|  	| 16.  Model selection 	|  	|
+|  	|  	| 17.  Validation and testing 	|  	|
+
+
 ## References:
 
   - ["Pattern Recognition and Machine Learning", C.M. Bishop, 2nd Edition, Springer, 2011.](https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf)