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Topic

MODULE 1 - PRIMER

1. Introduction to Machine Learning

a. What is Machine Learning
b. Types of Learning (Supervised, Unsupervised, Reinforced)
c. Structured vs Unstructured Data
d. Machine Learning and the World today!
e. Specific Use cases in Finance

2. Math toolbox for ML
a.Linear Algebra
i. Vector Algebra (Addition, Product, Projections)
ii. Matrix Algebra (Transpose, Multiplication, Inverse, Eigen Values)
b. Optimization
i. Maxima and Minima (calculus based)
ii. Lagrangian Multipliers
iii. Gradient Descent
c. Parameter Estimation
i. Maximum Likelihood Method (MLE)
ii. Maximum a Posteriori (MAP)

3.Getting Started with Python

a.Importing Libraries
b. Data types and Functions
c. Data Preprocessing (Missing Data, Categorical Encoding)
d. Splitting to Training, Validation and Testing sets
e. Feature Scaling

Module 2 – Supervised Learning (Regression)

4. Linear Regression

Introduction and Objective
Cost Function (MSE vs MAD)
Simple vs Multiple Linear Regression
Regression Assumptions (Multicollinearity, Exogeneity, Serial Correlation, Homoscedasticity)
Parameter Estimation (Analytical and Gradient Descent)

5.Stepwise Regression for High Dimensional Data

Forward Selection
Backward Elimination
Least Angle Regression (LARS)

6. Polynomial Regression

7. Support Vector Regression

Linear SVR
Kernel SVR

8. Decision Tree Regression

Splitting, Stopping
Bagging and Boosting

9. Random Forest Regression

10. Regression Model Selection and Performance

9. K - NEAREST NEIGHBOUR

1.K-Nearest neighbour
2.K-means protype

10. Bayes Theorem

Module 3 – Supervised Learning (Classification)

11. Supervised Learning – Classification

Introduction and Objective
Loss Functions (Logistic Loss, Hinge Loss)
Confusion Matrix and ROC

12. Classification – Logistic Regression

Logistic Regression
Multinomial Logit
Multiordinal Logit

13. K-Nearest Neighbor (kNN)

14. Naïve Bayes Classifier
15. Linear and Quadratic Discriminant Analysis (LDA)

16. Support Vector Machines

17. Decision Tree Classification

18. Random Forest Classification

19. Classification Model Selection and Performance

Module 4 – Anomaly Detection

20. One Class SVM

21. Isolation Forest

Module 5 – Unsupervised Learning
22. Dimensionality Reduction

Principal Component Analysis (PCA)
Kernel PCA

23. Clustering

Hierarchical Clustering
K-Means Clustering
Density Based Clustering

Module 6 – Reinforcement Learning

24. Upper Confidence Bound

25. Thompson Sampling

Module 7 – Natural Language Processing (NLP)

26. Processing Unstructured Data

27. Word2Vec

28. Feature Selection (Chi-sq and MI)

Module 8 – Deep Learning

29. Artificial Neural Network (ANN)

30. Recurrent Neural Network (RNN) and Long short term Memory (LSTM)

31. Convolutional Neural Network (CNN)