ML Techniques: Course Overview
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# ML Techniques: Course Overview ## 🎯 Learning Objectives - Build on ML Foundations with deeper mathematical rigor - Master regularization theory, Naive Bayes, neural networks, kernel methods, time series, anomaly detection, and semi-supervised learning - Understand ML system design principles ## 📖 Core Content ##...
ML Techniques: Course Overview
🎯 Learning Objectives
- Build on ML Foundations with deeper mathematical rigor
- Master regularization theory, Naive Bayes, neural networks, kernel methods, time series, anomaly detection, and semi-supervised learning
- Understand ML system design principles
📖 Core Content
Overview of Topics
| Topic | Prerequisites | Why It Matters |
|---|---|---|
| Regularization Theory | Linear models | Preventing overfitting, feature selection |
| Naive Bayes | Probability theory | Text classification, spam filtering |
| Neural Networks | Linear algebra, calculus | Deep learning foundation |
| Kernel Methods | SVM, linear algebra | Non-linear patterns |
| Time Series | Statistics | Forecasting, trend analysis |
| Anomaly Detection | Statistics, unsupervised learning | Fraud detection, monitoring |
| Semi-supervised Learning | Supervised + unsupervised | Leveraging unlabeled data |
| ML System Design | All ML concepts | Production ML |
This course is the bridge between understanding individual algorithms (ML Foundations) and deploying them in production systems (ML Practice). Each topic is covered with deeper mathematical rigor than MLF.
Course Prerequisites
- BSCS2004 (ML Foundations) — all 12 weeks of content
- Probability & Statistics — Bayes' theorem, distributions, hypothesis testing
- Linear Algebra — matrix operations, eigendecomposition, norms
- Calculus — partial derivatives, chain rule (especially for neural networks)
Let's begin with Regularization Theory — the mathematical foundation for preventing overfitting.
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