Day #29 - Decision Trees

• Hierarchical, Divide and Conquer strategy, Supervised algorithm
• Works on numerical data
• Concepts discussed - Information gain, entropy computation (Shanon entropy)
• Pruning based on chi-square / Shannon entropy
• Convert all string / character into categorical / numerical mappings
• You can also bucketize continuous variables

Basic Python pointers

Good Reads
Link1 , Link2, Link3, Link4, Link5, Link6

Happy Learning!!!

Day #28 - R - Forecast Library Examples

Following Examples discussed. Library used - R - Forecast Library

• Moving Average
• Single Exponential Smoothing - Uses single smoothing factor
• Double Exponential Smoothing - Uses two constants and is better at handling trends
• Triple Exponential Smoothing - Smoothing factor, trend, seasonal factors considered
• ARIMA

Happy Learning!!!

Applied Machine Learning Notes

Supervised Learning

• Classification (Discrete Labels)
• Regression (Output is continuous, Example - Age, Stock prices)
• Past data + Past Outputs used

Unsupervised Learning

• Dimensionality reduction (Data in higher dimensions, Remove dimension without losing lot of information)
• Reducing dimensionality makes it easy for computation (Continuous values)
• Clustering (Discrete labels)
• No Past outputs, Only current data

Reinforcement Learning

• All Game Playing is unsupervised
• Learning Policy
• Negative / Positive reward for each step

Type of Models

• Inductive (Learn model, Learn from a function) vs Transductive (Lazy learning ex- Opinion from like minded people)
• Online (Learn from every new incoming tweet) vs Offline (Look past 1 Yeat tweet)
• Generative (Apply Gaussian on Data, Use ML and compute Mean / Variance) vs Discriminative (Two sides of Line)
• Parametric vs Non-Parametric Models

Happy Learning!!!

Fifth Elephant Day #2

Fifth Elephant Day #2 - Part I

Session #1 - Content Marketing

• Distribute relevant consistent content. Traditional vs Content Marketing

Challenges

• Delivering content with speed. Channel proliferation (mobile, computers, tablets)
• Intersection of Brands, Trends, Community Interests (Social media post and metrics)
• Data from social media pages, online aggregators

Technical Details

• Computation of term frequency, inverse document frequency
• Using Solr, Lucene for Indexes
• Cosine Similarity
• Greedy Algorithm

Session #2 - Reasoning

• Prediction vs Reasoning problem
• Prediction Problems Evolution 
• At Advanced level Deep Learning, XGBoost, Graphical models

When Apply prediction ?
Features as input -> Prediction performed (Independent, stateless)

Reasoning - Sequential, Stateful Exploration
Reasoning Problems - Diagnosis, routes, games, crossing roads

Flavours of Reasoning

• Algorithmic (Search)
• Logical reasoning
• Bayesian probabilistic reasoning
• Markovnian reasoning

Knowledge, Learning the process of reasoning, Knowledge graphs were should in implementation of reasoning
{subject, predicate, object}

Session #3 - Continuous online learning

• 70% noise in C2B communication
• 100% noise in B2C communication
• Zipfian

Technicalities

• Apriori - Market Basket Analysis
• XGBoost - Alternative to DL
• Bias - Variance Tradeoff
• Spectral Clustering

Bird of Feathers Session

• Google Deepmind (Used for Air conditioning)
• Bayesian Probabilistic Learning
• Deep Learning - Build Hierarchy of features (OCR type of problems)
• Traditional Neural Network (Fully Connected, lot of degree of freedom)
• Structural causality (Subsystem appears before, Domain knowledge)
• Temporal causality - This and then that happened
• CNN - learning weights
• Spectral clustering
• PCA (reduce denser to smaller)
• Deep Learning - Hidden layers obtained through coarse grained process

Deep Learning workshop Notes

• Neural Networks
• Multiple Layers
• Lots of data

People Involved - Hinton, Andrew Ng, Bengio, Lecuss

Deep Learning now

• Speech recognition
• Google Deep Models on Phone
• Google street view (House numbers)
• Imagenet
• Captioning images
• Reinforcement learning

Neural Networks

• Simple mathematical units combine into complex functions
• X-> input, W-> weights, Non linear function of output

Multiple Layers

• Multiple hidden layers between input and output
• Training hidden layers is challenge

Gradient Descent

• Define loss function
• Minimize by moving along gradient

Backpropagation

• Move Errors back through the network
• Chain rule conception

Tools

• Cafee - Configuration file
• Torch - Describe network in lue
• Theano - Describes computation, writes cuda code, runs and gives results

CNN

• Used for images
• Images are organized
• Apply Convolutional filter
• For Deep Learning GPU is important

Imagenet Competition

• Convolution (Have all nice features retain them)
• Pooling (Shrink image)
• Softmax
• Other

Simplest RNN - Gradient Descent problem
LSTM (Long Short Term memory)
Interword relationships from corpus (word2vec)

Happy Learning!!!

Fifth Elephant Day #1 Notes - Part II

Sessions # - Link

Talk #3 - Machine Learning in FinTech

• Lending Space
• Credit underwriting system

India

• 2% Credit card usage
• 65% of population < 27 yrs
• Digital foot print (mobile)
• Identity (Aadhar)

40 Decisions / Minute -> 100 Crores a month

Use Cases / Scenarios

• Truth Score (Validity of address / person / sources)
• Need Score (Urgency / Time to respond application)
• Saver Score (cash flow real-time analytics)
• Credit Score (Debt to income)
• Credit awareness score
• Continuous risk assessments

Talk #4 - Driving Behaviour from Smartphone Sensors

• For Safety driving using smartphone sensors
• Spatial / location data
• Road traffic injuries due to distracted driving
• Phone usage - 4x crash risk
• Speedy driving - 45% car crash history
• Driving behavior analysis / driving feedback
• GPS + Inertial Navigational sensors (Accelerometer / Gyroscope / Magnetometer)

Characterization

• Drive detection
• Event detection
• Collision detection

Qualification

• Drive summarization and scoring
• Risk modelling

Optimization

• Events, location of events, duration of events

Dynamics

• Sensors
• Availability - wide variety across devices
• Raw Data - noisy, unevenly spaced time series
• Events - Time scales, combination of sensors
• Model building - Labelled vs unlabelled data, feature engineering
• Algorithms - Stream / batch efficiency

Techniques

• Cluster data 
• Eliminated uninteresting time periods
• Classification / Regression models
• Spectral clustering

Talk #5 - Indian Agriculture

• Crop rotation literacy
• Data curation, Query tools on data product
• Visualization and plotting of Agricultural data

Tak #6 and #7 - Last two talks were from Ecologists

• Using Image comparison for Big Cat Counting
• Predicting Big Cat Areas (Territories)
• Observe Nature, Frame Hypothesis, Design Experiments
• Confront with competing hypothesis
• Spacegap program
• Markov chain Monte-Carlo technique

Happy Learning!!!

Fifth Elephant Day #1 Notes - Part I

Sessions # - Link

Talk #1 - Data for Genomic Analysis

Great talk by Ramesh. I had attended his session / technical discussion earlier. This session provided insights on genome / discrepancies in genome sequence leading to rare diseases.

Genome - 3 Billion X 2 Characters
Character variables varies from person to person
Stats (1/10th of probability of cancer)
Baseline risk for breast cancer (1/8),(1/70) ovarian cancer
BRCA1 mutation (5-6 fold increase in breast cancer, 27 fold increase for ovarian cancer)

In India

• 35% inherited risk mutation
• 1/25 Thalassemia 
• 1 in 400-900 Retinitis Pigmentosa
• 1 in 500, Hypertrophic Cardiomyopathy

Data Processing

• 1 Billion reads - 100GB data per person
• Very similar sequence yet one character might differ
• But reference is 3 Billion long

Efficiency

• Need fast indexing
• Suffix Trees and variations
• Hash table based approaches

Reference Genome Sequence

• Volume of data
• Funnel down of variety of dimensions
• Triplet Code (Molecule)
• Variants of Triplets nailed down to difference of gnome
• GPU processing / reduce computation time

Concepts Discussed / Used

• Hypothesis Testing
• Stats Models
• GPU Processing to reduce computation time

They also provide assessment for hereditary diseases at corporate level.

Talk #2 - Alternative to Wall Street Data

This session gave me some new strategies to collect / analyze data

How to Identify occupancy rate at hotel ?

•  Count of cars from parking lots
•  Number of rooms lights on
•  Take pics of rooms from corner of street and predict based on images collected
•  Unconventional ways to think of data collection (Beating the wall street model)

What are usual ways

•  Checking websites

From Investor perspective lodging key metrics is a very important aspect
Data Sources

• Direct data gathering
• Web harvesting
• Primary research

Primary Research

• Look at notice patterns in front of you
• Difference in invoice numbers
• Serial number changes, difference values

Free Data Sets in link
Lot of opportunity

• Analyze international markets (India / China)
• COGS
• SG
• ETC

How to value data sets ?

• Scarcity - How widely used
• Granularity - Time / aggregation level
• Structured
• Coverage

What is the generative value

• Revenue Surprise Estimates
• Dataset insight / Analysis
• Operating GAAP measures

A Great case study on impact of smart watch vs luxury watch was presented ? This session provides great insight into unconventional data collection ways

• Generate money in automated system
• Stock sensitivity to revenue surprises
• Identify underlying ground truth

"Some Refreshing changes to world of investment"

Happy Learning!!!

Day #27 - Exploring ggplot2

Good Read - Design Patterns

Happy Learning!!!

Day #26 - R - Moving Weighted Average

Example code based on two day workshop on Azure ML module. Simple example storing and accessing data from Azure workspace

Happy Learning!!!

Day #25 - Data Transformations in R

This post is on performing Data Transformations in R. This would be part of feature modelling. Advanced PCA will be done during later stages



Data Normalization in Python

Happy Learning!!!