Overview
Explore the mathematical foundations of machine learning, from supervised to unsupervised learning techniques, to gain a deeper understanding of neural networks and their underlying processes.
Syllabus
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- Introduction to Machine Learning Mathematics
-- Overview of Machine Learning and Mathematical Prerequisites
-- Linear Algebra Basics: Vectors, Matrices, and Operations
-- Probability and Statistics Fundamentals
- Supervised Learning Foundations
-- Linear Regression: Least Squares, Cost Function, and Gradient Descent
-- Logistic Regression: Sigmoid Function, Loss Function, and Maximum Likelihood
-- Support Vector Machines: Margin, Dual Formulation, and Kernel Trick
- Unsupervised Learning Techniques
-- Clustering Algorithms: K-Means and Hierarchical Clustering
-- Principal Component Analysis (PCA): Eigenvectors and Eigenvalues
-- Gaussian Mixture Models and Expectation-Maximization
- Neural Networks and Deep Learning
-- Perceptron Model and Multilayer Perceptrons
-- Backpropagation and Chain Rule of Calculus
-- Activation Functions: Sigmoid, ReLU, and Softmax
- Optimization and Training Techniques
-- Stochastic Gradient Descent and Variants
-- Learning Rate Schedules and Regularization Techniques
-- Overfitting and Underfitting: Bias-Variance Tradeoff
- Advanced Topics in Machine Learning
-- Convolutional Neural Networks: Convolutions, Pooling, and Leveraging Image Data
-- Recurrent Neural Networks: Time Series and Sequence Data
-- Introduction to Reinforcement Learning Basics
- Mathematical Exploration of Performance and Evaluation
-- Confusion Matrix, Precision, Recall, and F1 Score
-- Receiver Operating Characteristic (ROC) and AUC
-- Cross-Validation and Model Selection Strategies
- Course Conclusion and Capstone Project
-- Integrating Mathematical Concepts in Real-world Applications
-- Building a Simple Machine Learning Model From Scratch
-- Discussing Future Trends and the Role of Mathematics in Advancing AI
Taught by
Tags
On-Demand
