Summary of SEM2 EECE 7130 Artificial Intelligence

iTaleem Video Tutorials Course Backup Guide Guidelines for Beginners

2024/2025 Academic Session 2023/2024 Academic Session 2022/2023 Academic Session 2021/2022 Academic Session 2020/2021 Academic Session

General

Announcements

EECE 7130 Artificial Intelligence (Course Outline)

Overview of Artificial Intelligence

Introduction and Motivation Understanding Artificial Intelligence

The History and Evolution of AI

Intelligent Agents

Definition of Agents

Rationality and Performance Measures

Nature and Types of Environments

Notes for Intelligent Agents

Supervised Learning (Regression Models)

Regression Models

Regression Models - Python Code

Linear Regression

Gradient Descent Algorithm

Multiple Linear Regression

Logistic Regression

Generalized Linear Models (GLMs)

Weighted Linear Regression (WLR)

Notes - Supervised Learning (Regression Models)

Supervised Learning (Probabilistic and Generative Models)

What is Gaussian?

What is Multivariate Gaussian?

Why Gaussian is often assumed?

How Gaussian (normal) distribution looks like in Python

Example of checking real world dataset - Iris flower dataset

What is Normality Test?

The Learning Roadmap to Gaussian Discriminant Analysis (GDA)

What is Probability, Conditional Probability and Bayes Theorem.

What is distribution?

What is Mean, Variance and Covariance

What is Classification?

What is Linear Algebra?

What is Bayes Theorem?

Linear Discriminant Analysis

Gaussian Discriminant Analysis

Naive Bayes Classifier

Bayesian Inference

Bayesian Networks (Bayes Nets)

Markov chain

Notes - Supervised Learning (Probabilistic and Generative Models)

Supervised Learning (Kernel Methods and Margin-Based Methods)

Introduction to Kernel Methods

Introduction to Margin-Based Methods

Support Vector Machines (SVMs)

Notes - Supervised Learning (Kernel Methods and Margin-Based Methods)

Supervised Learning (Tree-based Methods and Ensemble Methods)

Introduction to Tree-based Methods and Ensemble Methods

Decision Tree

Random Forest

Notes - Supervised Learning (Tree-based Methods and Ensemble Methods)

Unsupervised Learning (Clustering Techniques)

Introduction to Unsupervised Learning (Clustering Techniques)

K Means Clustering

What is Hierarchical Clustering

What is DBSCAN

Clustering Evaluation and Validation

Notes - Unsupervised Learning (Clustering Techniques)

Neural Networks

Why do We Need Neural Networks

Basic Building Blocks - The Artificial Neurons

Notes - Neural Networks

Backpropagation

Feedforward Neural Networks

Backpropagation

Notes - Backpropagation

Deep Learning

Deep Architectures

Convolutional Neural Networks (CNN)

Notes - Deep Learning

Formal Evaluation Techniques

Model Validation and Evaluation

Evaluation Metrics

Notes - Formal Evaluation Techniques

Login using your CAS account

Or enter your details to log in your account

SEM2 EECE 7130 Artificial Intelligence