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.
- 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 ?
- 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 ?
- Firstly how to do Feature Representation?
- Secondly what is Multi-Collinearity ?
- Following, Bias Vs Variance Trade Off ?
- Lastly, Lasso vs Ridge Regression ?
- Bagging vs Boosting ?
- What are Boosting Base Models?
- What is the Logit Function?
- Better Data vs Better Model ?
- Hessian Matrix vs Gradient Descent
- Formula description
- What is Derivation
- Correlation vs Covariance
- Covariance as graph
- What are Eigen Values Intuition
- What is a Value Vector
- Eigen Value as Hinge
- Laplace Transformation Intuition
- Transformation as a tool
- Lagrange Multiplier Intuition
- Constrained Optimisation Problem
- Shortest Path Algorithm
- Encoding the Problem as DAG
- Solving using Optrees
- Null Hypothesis
- Significane vs Non Significance
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.
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!!