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Syllabus

Syllabus

Introduction to Data Mining

The new premise
Data Science 360
Pipelines
- A Case Study of an ML Architecture - Uber
- The Machine Learning landscape

The Learning Problem

The Learning Problem
End-to-end Machine Learning project
Empirical Risk Minimization

Predictors for Structured Data

Decision Trees
Decision tree from scratch
Visualizing tree-based classifiers
Visualizing tree-based regressors
Ensemble Methods

Learning without Labels or Without Parameters

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

Representation Learning

Maximum Likelihood Estimation of a marginal model
Generative Modeling and Probabilistic Graphical Modeling
The Expectation - Maximization (EM) Algorithm
Expectation Maximization for Gaussian Mixture Model

Dimensionality Reduction

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

Regression and Classification

Linear Regression
Stochastic Gradient Descent
Introduction to Classification
Logistic Regression

Deep Neural Networks

The Neuron (Perceptron)
Fashion MNIST Case Study

Image Data and Convolutional Neural Networks

Introduction to Convolutional Neural Networks
CNN Architectures
CNN Example Architectures
Using convnets with small datasets
Feature Extraction via Residual Networks

Time-series data and Recurrent Neural Networks

Introduction to Recurrent Neural Networks (RNN)
Simple RNN
The Long Short-Term Memory (LSTM) Architecture
Time Series Prediction using RNNs

Neural Autoencoders

Variational Inference
VAE Architecture
Variational AutoEncoder

Math Background

Math for ML Textbook
Probability Basics
Linear Algebra for Machine Learning
Calculus

Resources

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

Assignments

Cutting Edge Development Environment for Data Science
Online Ensemble Learning from NYC Taxi Ride Event Streams
Logistic Regression

Project

News Time Machine

Repository
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Open issue

.md

The Expectation - Maximization (EM) Algorithm

The Expectation - Maximization (EM) Algorithm#

See in-person class notes for details. These notes will make it here during Spring 2024.

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By Pantelis Monogioudis, Ph.D

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