kingers Posted Tuesday at 03:26 PM Report Share Posted Tuesday at 03:26 PM Mathematical Introduction To Machine Learning Published 5/2025 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz Language: English | Size: 10.28 GB | Duration: 11h 15mA mathematical journey through common machine learning frameworks in regression, classification, and clustering. What you'll learn Learn basics of machine learning, including both supervised learning and unsupervised learning. Grasp the mathematical foundations of the most common machine learning framework. Be able to differentiate appropriate machine learning models for specific use cases (e.g. regression vs. classification vs. clustering). Have a well-tailored toolbox of machine learning algorithms to apply to data science problems. Be familiar with how to fit machine learning models in R and Python. Be familiar with the challenges ones can face in machine learning. Requirements Linear Algebra Probability Statistics Multivariate Differential Calculus Beginner experience in R Beginner experience in Python Description Are you ready to gain a deep and practical understanding of machine learning? This comprehensive course is designed to take you from the foundational principles of machine learning to advanced techniques in regression, classification, clustering, and neural networks. Whether you're a student, a data science enthusiast, or a professional looking to sharpen your skills, this course will give you the tools and intuition you need to work effectively with real-world data.We begin with a conceptual overview of machine learning, exploring different types of learning paradigms-supervised, unsupervised, and more. You'll learn how to approach problems, evaluate models, and understand common pitfalls such as overfitting, bad data, and inappropriate assumptions.From there, we dive into regression, covering linear models, regularization (Ridge, LASSO), cross-validation, and flexible approaches like splines and Generalized Additive Models-all illustrated with hands-on examples using datasets like Gapminder and Palmer Penguins.Classification techniques are covered in depth, including logistic regression, KNN, generative models, and decision trees, along with neural networks and backpropagation for more advanced modeling.Finally, we explore clustering, from k-means to hierarchical methods, discussing algorithmic strengths, challenges, and evaluation techniques.With real-world datasets, detailed derivations, and clear explanations, this course bridges the gap between theory and application. Overview Section 1: Introduction to Machine Learning Lecture 1 Outline Lecture 2 Overview of Machine Learning Lecture 3 Supervised Learning Introduction Lecture 4 Why Test Data? Lecture 5 Unsupervised Machine Learning Lecture 6 Other Types of Learning Lecture 7 Supervised Learning Example: Mushroom Dataset Lecture 8 Machine Learning Issues: Bad Data Lecture 9 Machine Learning Issues: Under-Over fitting Lecture 10 Intro to Machine Learning Formalism Lecture 11 Model Evaluation Lecture 12 Machine Learning Trade-Offs Lecture 13 Estimating the Regression Function Lecture 14 More Complex Regression Functions Lecture 15 The Bias-Variance Trade-Off Section 2: Introduction to Regression Models Lecture 16 Outline Lecture 17 Intro and Motivating Example Lecture 18 Intro to Simple Linear Regression Lecture 19 With Intercept Model Lecture 20 Example: Gentoo Penguins Lecture 21 Derivation: Multiple Linear Regression Lecture 22 Example: Gapminder Lecture 23 Interpretation of OLS Output Lecture 24 Hypothesis Testing Lecture 25 Confidence Intervals Lecture 26 Model Evaluation Lecture 27 Feature Selection Lecture 28 Other Questions Section 3: Regularization & Other Regression Variants Lecture 29 Intro to Regularization Lecture 30 Ridge Regression Lecture 31 Best Subset Selection Lecture 32 LASSO Regularization Lecture 33 Other Regression Variants Lecture 34 Example: Gapminder Regularized Regression Section 4: Cross-Validation Lecture 35 K-Fold Cross Validation Lecture 36 Cross Validation on Gapminder Lecture 37 Hyperparameter Selection for Regularization Section 5: Non-Linear Modelling & Regression Variants Lecture 38 Non-Linear Modelling and Basis Functions Lecture 39 Example: Polynomial Gapminder Lecture 40 Step Functions Lecture 41 Example: Gapminder Step Function Regression Lecture 42 Regression Splines Lecture 43 Example: Gapminder Splines Lecture 44 Smoothing Splines Lecture 45 Example: Gapminder Smoothing Splines Lecture 46 Generalized Additive Models Lecture 47 Example: Gapminder Section 6: General Regression Models and AutoML Lecture 48 General Model Selection Lecture 49 Example: Gapminder AutoML Section 7: Introduction to Classification Lecture 50 Outline Lecture 51 Introduction to Classification Lecture 52 Formalized Classification Setup Lecture 53 Classification Performance Evaluation Section 8: KNN and OLS for Classifiaction Lecture 54 KNN & Bias Variance Tradeoff Lecture 55 Comparison: KNN vs. OLS Lecture 56 Example: Gapminder 1 [Introduction to Dataset and Classification Approach] Lecture 57 Example: Gapminder 2 [Classification in R] Lecture 58 Example: Gapminder 3[ Building OLS Classifier] Section 9: Logistic Regression Lecture 59 Intro to Logistic Regression Lecture 60 Formalizing Binary Logistic Regression Lecture 61 Example: Credit Defualt Classification Lecture 62 Warning: Confounding Lecture 63 Multinominal Logistic Regression Lecture 64 Example: Palmer Penguins Section 10: Generative Models Lecture 65 Intro to Generative Models Lecture 66 Gaussian Bayes Derivation Lecture 67 Quadratic Discriminant Analysis Lecture 68 Linear Discriminant Analysis (LDA) Lecture 69 Naive Bayes Classifiers (NBC) Lecture 70 Example: Palmer Penguins QDA Lecture 71 Example (cont'd): Palmer Penguins LDA and Naive Bayes Section 11: Tree-Based Learning Lecture 72 Introduction to Tree Based Methods Lecture 73 Example: Gapminder 1 [Building the Model] Lecture 74 Example: Gapminder 2 [ Analyzing the Model] Lecture 75 Building a Regression Tree Lecture 76 Tree Pruning Lecture 77 Classification Trees Lecture 78 Example: Iowa Housing Data Section 12: Neural Networks Lecture 79 Intro to Neural Networks & Activation Functions Lecture 80 Derivation: Fully Connected Feed Forward Neural Networks pt1 Lecture 81 Derivation: Fully Connected Feed Forward Neural Networks pt2 Lecture 82 Derivation: Fully Connected Feed Forward Neural Networks pt3 Lecture 83 Example: Computer Vision w/ Neural Networks Section 13: Introduction to Clustering Lecture 84 Outline Lecture 85 Clustering Algorithms and Theory Lecture 86 Generalities Lecture 87 Clustering Framework & Applications Lecture 88 What is a Cluster? Lecture 89 Clustering Approaches Lecture 90 Distance, Similarity, and Dissimilarity Lecture 91 Data Transformations for Clustering Lecture 92 Challenges in Clustering Section 14: K-Means Clustering Lecture 93 k-Means Clustering Lecture 94 k-Means Algorithm Lecture 95 Strengths and Limitations of k-Means Lecture 96 Example: Penguins Dataset Lecture 97 Example: Gapminder Dataset Section 15: Hierarchical Clustering Lecture 98 Introduction to Hierarchical Clustering Lecture 99 Introduction to AGNES and DIANA Lecture 100 A Formal Look into AGNES and DIANA Lecture 101 Linkage Strategies Lecture 102 Example: Penguins Dataset Lecture 103 Example: Gapminder Dataset Section 16: Clustering Evaluation Lecture 104 Intro to Clustering Evaluation Lecture 105 Clustering Assessment Lecture 106 Clustering Quality Measures Lecture 107 Internal Validation Lecture 108 Cluster Quality Metrics Lecture 109 Relative Validation Lecture 110 External Validation and Model Selection Future machine learning 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