I have been an avid learner of statistics and machine learning. My primary reason for exploring them has been the ability to apply them to use-cases cutting across different domains and problems. Primary interest though pertains to the market place, trading, pricing, optimisation problems.

I have had the opportunity to explore **healthcare, trading and sports analytics** in the form of cricket, where I have been able to both formulate and solve a bunch of interesting problems end to end. These notes try to pen down the intuition behind several of those questions. **The notes link is embedded in the Title itself. **

Questions : Machine Learning Basics

**Gradient Descent**

**Convex** and **Non Convex Problems ?** - Relationship between
**Rate Of Learning** and **Step Size** ? - Gradient Descent vs
**Stochastic** Gradient Descent ?

**Data Modelling**

- Feature selection, transformation and extraction
- Learning vs
**Memoization** ? - What is the assumption behind
**IID, stationarity** and same sample data ? - Train vs Validation vs Test Sets ?

**Feature Regularisation**

- Feature Representation
- What is
**Multi-Collinearity** ? **Bias Vs Variance** Trade Off ? - Lasso vs Ridge Regression ?

**Algorithms**

- Bagging vs Boosting ?
- Boosting
**Base Models** - Logit Function
**Better Data** vs Better Model ?

Machine Learning Essentials

**Hessian Matrix**

**Hessian Matrix vs Gradient Descent** - Formula

**Variance **

- Derivation
**Correlation vs Covariance** - Covariance as graph

**Eigen Values**

- Eigen Values
**Intuition** - Eigen Value Vector
- Eigen Value as Hinge

**Laplace Transformation **

- Laplace Transformation
**Intuition ** - Transformation as a tool

**Lagrange Multipliers**

- Lagrange Multiplier
**Intuition** **Constrained Optimisation** Problem

Data Science with R (Scheduling using Optrees)

- DAG
- Shortest Path Algorithm
- Encoding the Problem as
**DAG** - Solving using Optrees

Machine Learning Regression (Interpretation)

- Null Hypothesis
- Significane vs Non Significance
- Intercept
- Interpretation

In the process, I would often end of re-using similar problem-solving approaches and often wanted to remember all the cool tricks/hacks and hard-learned concepts I have had been able to work-out all these years.

**Auquan Financial Time Series**

And several other contests during campus days !!

Besides improving your odds of winning these, I decided to write some notes to help collate concepts and takeaways from ML in an intuitive fashion. If you are one of those guys who love diagrams and intuition over maths to better understand stuff, you would love them.

In case of any doubts or questions, feel free to reach out below!!

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