Theory ofIntelligence
Join Class Waitlistauto_awesomeAsk TOImailContact

Master

Supervised Learningfirst principles.

MACHINE LEARNING • CONCEPTS FIRST.

Chapter-by-chapter theory notes, step-by-step textbook exercises, video lectures, and SLO-aligned board examination prep for Supervised Learning (Machine Learning).

Start First Chapter→Browse Chapters↓
5
Textbook Chapters
21
Lessons & Exercises
100%
Solved Derivations
0%
0 / 21 Completed
format_list_bulletedCurriculum & Chapterssmart_displayVideo Lecturesaccount_treeCurriculum Syllabusfolder_openExam Prep & Resources
ML Foundations (17)Supervised Learning (21)Classic Algorithms (18)Unsupervised Learning (14)Advanced & Production ML (14)
Syllabus

Curriculum Syllabus

Course chapter sequence roadmap and learning outcomes.

Supervised Learning (Machine Learning)

1. Classification

1.1 Training a Binary Classifier
1.2 Multiclass Classification
1.3 Multilabel & Multioutput

2. Evaluating Classifiers

2.1 Accuracy & the Confusion Matrix
2.2 Precision, Recall & F1
2.3 The Precision/Recall Trade-off
2.4 ROC Curve & AUC
2.5 Error Analysis

3. Linear Regression

3.1 The Linear Model
3.2 The Normal Equation
3.3 Gradient Descent
3.4 Polynomial Regression
3.5 Learning Curves

4. Regularized Linear Models

4.1 Ridge Regression
4.2 Lasso Regression
4.3 Elastic Net
4.4 Early Stopping

5. Logistic and Softmax Regression

5.1 Estimating Probabilities
5.2 Training & Cost Function
5.3 Decision Boundaries
5.4 Softmax Regression
INTERACTIVE LAB • 18 STEM WORKSTATIONS

Bridge High-School Science to Computational AI

Connect textbook physics, mathematics, and computing to real-world artificial intelligence models and symbolic solvers.

scienceOpen Interactive Laboratories→Board MCQs & Past Papers