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diff --git a/SecondSaturday/Notes.md b/SecondSaturday/Notes.md
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+1. flowchart application -> ios mobile find symbol through gestures.
+2. matrimony portal profiles -> finding matching profiles
+3. swipe input keyboard for indian languages.
+4. mnist hand-written digit database -> build application for recognizing full phone numbers(10 digit).
+5. live cricket feed -> generating highlights of the match.
+6. designing a chatbot for getting cricket scores.
+
+
+# general approach to machine-learning
+1. model, objective and learning algo
+3. ml is technique for learning from examples
+    -> recommending smart-phone to a friend. price, branch, cam quality, screen size, processing speed. ==> model
+    -> objective ( why is he buying the phone) should be able to be boiled-down to a single number.
+    -> learning algo
+    binary features -> camera 0|1
+                    -> screen small|large
+                    -> battery good|bad
+                    -> memory high|low
+                    -> processing fast|slow
+                    -> audio good|bad
+
+prior probability -> probability of an event occuring without any knowledge of conditions
+
+P(A)*P(B|A) = P(B)*P(A/B) = P(A,B)
+
+P(+ve|[x1,x2,x3...xn]) = P([x1,x2...xn]|+ve)*P(+ve)/P([x1,x2...xn])
+                       = P(x1|+ve)*P(x2|+ve)*P(x3|+ve)*P(+ve)/P([x1,x2...xn])
+                       = Pi (i=1 to n) P(xi|+ve) * P(+ve)(Class variable)/ Sum (C=+ve to -ve) (P([x1,x2....xn],C))
+                       = Pi (i=1 to n) P(xi|+ve) * P(+ve)(Class variable) / (( Pi (i=1 to n ) P(xi|+ve)*P(+ve))+..+ Pi (i=1 to n) P(xi|-ve)*P(-ve))
+
+P(X,Y) = Sum (y=y1...yn) P(X,Y=y)
+
+W2 = P(+ve|xi=1) (human approach) (Naive bayes) (naively thinking that all features are independent)
+
+
+Regression : output contains real values -> (predicting the position of joints of a body given an image of a person)
+Classification : output classifies to discrete set of classes
+      -> predicting the posture of a person (sitting,walking,standing,running) given an image of a person
+(Numerical/Categorical)
+
+Representation Learning: embedding
+
+Deeplearning is all about hierarchical representation learning.
+
+Metric Learning: distance( of facial features)/similarity(of fashion apparels)/relevance( of search document)
+Structured Output(Models): auto-corrects dependent outputs based on output on the upper hierarchy outputs.
+
+Types of input:
+Bag of features,bag of words: ( finding whether a feature is present/not without caring where the feature occurs in the input)
+eg: Using unsupervized learning to convert the input to a given set of classes(clusters) and use them as bag of features.
+
+Spatial data(sequential data): if there local dependencies use CNN(convolutional nn) if there are near past dependencies in the data use RNN(Recurrent NN-LSTM)
+eg: stock market temporal data / speech data/ image data
+
+Non-Parametric models : k-NN(K-nearest neighbor), Decision Trees, Random Forests (independent of parameters)
+  -> very inaccurate because doesn't know much about the data
+Parametric Models: based on fixed set of parameters
+  -> more accurate coz the knows more about the parameters from the data
+
+
+Types of Learning:
+  supervized learning -> labeled data
+  unsupervized learning -> unlabeled data
+    exercise: *take 3s from mnist data *create a gmm model with them and *cluster them with 5/3/10 gaussians.
+              *take all images and cluster them to 10 gaussians.
+  semi-supervized learning -> combination of supervized and unsupervized models
+
+  Auto Encoder:
+    finding a low dimensional representation of a high dimensional data.
+    eg. image of 200x200 pixels create a fingerprint of image of 128 dimensions.
+       exercise: use the 128 dimensional data to reconstruct the 200x200 image(using inverse of the model).
+
+  Reinforcement learning:
+    eg:playing chess -> using the final result of the game to assign weights/score for moves that were made upto the final result. and training the model to predict based on those scores.
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+# swipe input keyboard for indian languages.
+given a gesture made on the keybard,language chosen-> keyboard layout
+predict the word that matches closest.
+
+
+## Input
+gesture data -> polygon(shape,size,corners, path), (time, pauses)?, spatial data with word character correlation.
+weighted-vocabulary,corpus for the language,history of gesture-word mappings/corrections for the user.
+language, keyboard layout
+
+## Output
+  Predict the word
+
+## Model
+  Structured Output/HMM/ CNN?
+
+
+# mnist hand-written digit database -> build application for recognizing full phone numbers(10 digit).
+
+## Input
+mnist digit database, generated 10 digit images with random positioning,orientation,scale of individual digit images sampled randomly from the mnist database.
+
+## Output
+  predict the phone number
+
+## Model
+  regression model to identify the points where the split for the images has to be made and pass the
+  split images to mnist digit recognizer to identify the digit.