About the IATACF
Module 4 – Machine Learning and Data Science
Module four will provide you with an introduction to cutting-edge data science and machine learning techniques applied in the field of finance. It commences with a thorough examination of the subject, encompassing fundamental mathematical tools, and subsequently delves deeply into supervised learning. This in-depth exploration encompasses regression methods, k-nearest neighbors, support vector machines, ensemble methods, and a range of other advanced topics.
Machine Learning Fundamentals - Part I
- Definition of Mathematical Modeling
- Traditional Modeling Approaches
- Distinguishing Machine Learning
- Fundamental Machine Learning Techniques
Machine Learning Fundamentals - Part II
- Common Machine Learning Terminology
- Introduction to Supervised Learning Methods
- Introduction to Unsupervised Learning Methods
- Introduction to Reinforcement Learning Methods
Mathematical Foundations for Machine Learning
- Understanding Learning Theory: The Bias-Variance Dilemma
- Essential Linear Algebra for Machine Learning
- Exploring Empirical Risk Minimization
- Mastering Gradient Descent Techniques: Stochastic and Accelerated Variants
- Applying Constrained Optimization in Machine Learning
- Delving into Probabilistic Modeling and Inference
- Unpacking the Power of Gaussian Processes
- Navigating the Art and Science of Model Selection
Predictive Modeling with Supervised Learning
- Proximity-Based Regression
- Regularized Regression Techniques: Lasso, Ridge, and Elastic Net
- Logistic and Softmax Regression
Advanced Supervised Learning Techniques
- K Nearest Neighbors
- Naïve Bayes Classifier
- Support Vector Machines
Decision Trees and Ensemble Learning
- Introduction to Decision Trees
- CART: Classification and Regression Trees
- Performance Metrics for Trees (Entropy, Gini Impurity)
- Fitting Decision Trees to Data
- Balancing Bias and Variance in Decision Trees
- Bootstrap Aggregating (Bagging) for Variance Reduction
- Random Forests
- Boosting Techniques for Bias Reduction
- Generic Boosting (Anyboost)
- Gradient Boosted Regression Trees
- Adaptive Boosting (AdaBoost)
- Finance Applications
Applied Machine Learning in Finance: Case Studies
- Predicting the S&P 500 and Baa-Spread through Macro Forecasting
- Analyzing Mutual Funds with Sharpe Style Regression Techniques
- Employing Natural Language Processing to Evaluate Sentiment in ESG Company Reports
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