Data Mining for Smart Cities 

Course Introduction

Meet Your Instructor

Course Overview

Necessity of Data Mining

Data and Mining Techniques

Applications of Data Mining to Smart Cities

Data Mining: Common Challenges

Uncertainty and How to Model It

Review of Random Variables

Population, Samples, and Statistical Inference

Parameter Estimation

Types of Collected Data

Data Quality

Data Preprocessing Tasks

Task Identification

Essential Reading: Introduction to Data Mining and its Application to Urban Systems

Essential Reading: Review of Statistical Methods

Recommended Reading: Review of Statistical Methods

Essential Reading: Data Pre-Processing - I

Introduction to Data Mining for Smart Cities

Graded Quiz: Introduction to Data Mining for Smart Cities

Installing Python Using Anaconda Distribution

Python Data Types and Data Structures

Python Libraries for Data Mining: NumPy, SciPy, and Matplotlib

Feature Scaling and Standardization

Label Encoding for Categorical Variables

Handling Missing Values in the Data

Essential Reading: Python Programming for Data Mining

Essential Reading: Data Pre-Processing - II

Introduction to Project - Multi-layer Perceptron and Markov Process

Live Session 1

Introduction to Python Programming for Data Mining

Graded Quiz: Introduction to Python Programming for Data Mining

Regression Analysis and Its Applications in Smart Cities

Linear Regression Problem and Its Solution

Advanced Regression Models

Classification Problem and Logistic Regression

Naive Bayes Classifiers

Bayesian Network Classifiers

Decision Tree Classifiers and How to Train Them

Popular Decision Tree Algorithms and Their Shortcomings

Linear SVMs and How to Train Them

Nonlinear SVMs: The Kernel Trick

Ensemble Classifiers

Classifier Performance Evaluation

Essential Reading: Regression Analysis

Recommended Reading: Regression Analysis

Essential Reading: Introduction to Statistical Classifier

Essential Reading: Decision Trees

Essential Reading: Support Vector Machines (SVMs)

Supervised Learning

Graded Quiz: Supervised Learning

Introduction and Applications to Urban Systems

Association Rule Mining: Brute Force vs. Apriori

Generating Association Rules from Frequent Itemsets

Data Clustering and Similarity/Distance Measures

Distribution Model-Based Clustering Algorithms

Partitional Clustering Algorithms

Limitations of k-Means and Importance of Choosing Initial Centroids

Hierarchical Clustering

Density-Based Clustering Algorithms

Cluster Validity

Characteristics of Data, Clusters, and Clustering Algorithms

Essential Reading: Association Rule Mining

Essential Reading: Data Clustering

Recommended Reading: Data Clustering

Essential Reading: Evaluating the Results of Data Clustering

Live Session 2

Practice Quiz: Unsupervised Learning

Graded Quiz: Unsupervised Learning

The Anomaly Detection Problem

Anomaly Detection Techniques: Part 1

Anomaly Detection Techniques: Part 2

Statistical Significance Testing

Hypothesis Testing

Essential Reading: Introduction to Anomaly Detection

Essential Reading: Avoiding False Discoveries

Practice Quiz: Anomaly Detection and Result Validation

Graded Quiz: Anomaly Detection and Result Validation

Neuron and ANN Models

Multilayer Feed-Forward ANNs

ANN Applications

Introduction to Deep Learning

Training a Deep Neural Network

Deep Learning for Smart Cities

Markov Process

Graphical Models

Discrete-State HMMs

Applications of HMMs to Smart Cities

Essential Reading: Neural Networks

Essential Reading: Deep Learning

Essential Reading: Hidden Markov Models (HMMs)

Practice Quiz: Advanced Data Mining Techniques

Course Wrap-up

How to Attempt and Submit the Project

Live Session 3