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Introduction to Data Science

Syllabus

Syllabus

Introduction to Data Science

Course Introduction
Data Science 360
ML Pipelines
A Case Study of an ML Architecture - Uber

Probabilistic Learning

Probability Basics
Entropy
COVID-19 Antibody Test

The Learning Problem

The Learning Problem
Linear Regression
Stochastic Gradient Descent

Classification

Introduction to Classification
Logistic Regression

Trees and Intelligence of the Crowds

Decision Trees
Regression tree stumps
Ensemble Methods
Random Forests
Adaptive Boosting (AdaBoost)
Gradient Boosting
Boosting workshop

Non-Parametric Methods

K-means Clustering
k-Nearest Neighbors (kNN) Classification
kNN Workshop

Neural Networks

The Neuron (Perceptron)
Deep Neural Networks
Introduction to Backpropagation
Backpropagation in Deep Neural Networks
Backpropagation DNN exercises
Fashion MNIST Case Study
Regularization in Deep Neural Networks
Introduction to Convolutional Neural Networks
CNN Architectures
CNN Example Architectures
Using convnets with small datasets

Dimensionality Reduction

Introduction to Recommender Systems
The Netflix Prize and Singular Value Decomposition
Principal Component Analysis (PCA)
PCA Workshop

Math Background

Math for ML
- Linear Algebra for Machine Learning
- Calculus

Resources

Your Programming Environment
Submitting Your Assignment / Project
Learn Python
Notebook execution status

Assignments

Probability Assignment
Gaussian Maximum Likelihood
Simple Language Models

Project

Interpretable Gradient Boosting - Real Estate House Price Prediction

Repository
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Boosting workshop

Boosting workshop#

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Gradient Boosting

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K-means Clustering

By Pantelis Monogioudis, Ph.D

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