My Machine Learning Notes

📊 Statistical Inference

This course covers the mathematical foundations of classical and modern statistical inference. Topics include sampling distributions, confidence intervals, hypothesis testing, likelihood theory, and bootstrap methods as well as linear models. Emphasis is placed on conceptual understanding, simulation-based intuition, and practical implementation using R. It is foundational for understanding statistical learning and data science.

Reference: Mathematical Statistics with Resampling and R (3rd Edition) by Laura M. Chihara and Tim C. Hesterberg

Chapters:

• Chapter 1: Probability Review & Statistics
• Chapter 2: Exploratory Data Analysis
• Chapter 3: Hypothesis Testing: Permutation Tests
• Chapter 4: Sampling Distributions
• Chapter 5: Confidence Intervals: The Bootstrap
• Chapter 6: Maximum Likelihood and Moment Estimation
• Chapter 7: More Confidence Intervals
• Chapter 8: Hypothesis Testing: Type I & Type II
• Chapter 9: Regression: Correlation and Covariance
• Chapter 10: Categorical Data and Chi-Square Test
• Chapter 11: Bayesian Methods: Discrete and Continuous Prior

📘 Optimization Methods

This course introduces the fundamental theories and methods of optimization, including unconstrained and constrained optimization, convex analysis, gradient-based methods, and Lagrangian duality. The techniques learned here are widely applicable in areas such as machine learning, operations research, and engineering design.

Reference: Convex Optimization by Stephen Boyd and Lieven Vandenberghe

Chapters:

• Chapter 1: Introduction to Optimization
• Chapter 2: Linear Algebra Review
• Chapter 3: Convex Sets
• Chapter 4: Convex Functions
• Chapter 5: Optimization Problems
• Chapter 6: Duality Theory
• Chapter 7: Optimality Conditions
• Chapter 8: Gradient-Based Methods

🤖 Machine Learning

This course introduces the theoretical and practical aspects of machine learning, including supervised and unsupervised methods, linear models, classification, support vector machines, ensemble methods, and deep learning basics. It bridges the gap between theory and application for real-world AI systems.

Reference: Pattern Recognition and Machine Learning by Christopher M. Bishop

Chapters:

• Chapter 1: Introduction to Machine Learning
• Chapter 2: Linear Regression
• Chapter 3: Logistic Regression & Classification
• Chapter 4: Support Vector Machines
• Chapter 5: Decision Trees & Ensemble Methods
• Chapter 6: Unsupervised Learning (K-Means, PCA)
• Chapter 7: Neural Networks
• Chapter 8: Deep Learning & Representation Learning