Undoubtedly I have been an avid learner of statistics and machine learning. Also, 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 also. Being able to both formulate and solve a bunch of interesting problems end to end, some intuition around modelling and right practices was developed.

### Questions : Machine Learning Basics

**Gradient Descent**

- Firstly, what are
**Convex**and**Non Convex Problems ?** - Secondly, what is the Relationship between
**Rate Of Learning**and**Step Size**? - Lastly, Gradient Descent vs
**Stochastic**Gradient Descent ?

**Data Modelling**

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

**Feature Regularisation**

- Firstly how to do Feature Representation?
- Secondly what is
**Multi-Collinearity**? **Following, Bias Vs Variance**Trade Off ?- Lastly, Lasso vs Ridge Regression ?

**Algorithms**

- Bagging vs Boosting ?
- What are Boosting
**Base Models**? - What is the Logit Function?
**Better Data**vs Better Model ?

### Machine Learning Essentials

**Hessian Matrix**

**Hessian Matrix vs Gradient Descent**- Formula description

**Variance **

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

**Eigen Values**

- What are Eigen Values
**Intuition** - What is a 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

Consistently, I would often end up re-using similar problem-solving approaches. Likewise 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. Also takeaways from ML in an intuitive fashion. If you are one of those guys who love diagrams and moreover intuition over maths to better understand stuff, you would love them.

**Machine Learning/Deep Learning For Quant Finance**

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