Statistical Machine Learning (SML) Course

Welcome to the Statistical Machine Learning (SML) course! This repository contains Jupyter notebooks and labs designed for a 55-minute class session each.

About the Course

Machine Learning (ML) is a dynamic field at the crossroads of data science, mathematics, statistics, and computer science. It involves techniques that allow machines to learn from data and improve over time. This course integrates ML algorithms with statistical thinking, covering:

• 1. Basics of Python
• 2. Regression
• 3. Classification
• 4. Cross-validation and data transformations
• 5. Dimensionality reduction
• 6. Basics of deep learning
• 7. Optional Topics: Transformers

Course Materials

This course is continually updated. Here’s what we have so far:

Introduction to Jupyter Notebook

• 11 - Getting Started with Jupyter Notebook

• 12 - Python Data Types

• 13 - Python Loops

• 14 - Python Functions

• 15 - Python Basic Plotting

• 16- Basics of Numpy

• 17 Basics of Pandas

• Lab 1: Data Collection

• 21 - Simple Regression

• 22 - Model Accuracy I

• 23 - Model Accuracy II

• Lab 2: Simple Linear Regression

• 24 - Multiple Linear Regression

• 25 - Performance Metrics for MLR

• 26 - Lab 3: Feature/Model Selection

• 27 - Predictions in MLR

• 31 - Classification

• 32 - Multi-Class Classification

• 33 - Confusion Matrix

• 34 - K-Nearest_Neighbors

• 35 - Decision Trees

• 41- Stratified Splits

• 42- K-fold Cross Validation

• 51 - Principal Component Analysis (PCA)

• 52 - PCA: Dimensionality Reduction

• Lab 5: Movie Recommendation System.ipynb

• 61 - Neural Network

• Lab4: Multiclass Classification Using Tenswrflow.ipynb

• 71 - Natural Language Processing (NLP) with Transformers

• 72 - Music Genre Classification with Transformers

Reference

For further reading, we recommend:

• An Introduction to Statistical Learning

Feel free to explore the notebooks and labs, and check back for new content!