DATA4800

01

Introduction to Artificial Intelligence

January 10 - January 16, 2025

Welcome to DATA4800! This week we'll explore the fundamental concepts of artificial intelligence and its role in modern technology.

Course Syllabus

Detailed overview of course objectives, grading policy, and expectations.

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Lecture Slides

Introduction to AI concepts and history.

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Exercise 1

Introduction to AI concepts and applications.

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02

Introduction to Machine Learning

January 17 - January 23, 2025

This week we'll dive into the fundamentals of machine learning, exploring different types of learning and their applications.

Lecture Content

Machine learning fundamentals and types.

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Small Dataset

For Decision Treet.

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Orange Instruction

Introduction to ML with Orange.

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Practice Dataset - Retail Campaign Dataset

First touch with Orange

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Dataset for Quiz - Customer Churn Dataset

Use this for your quiz

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03

Supervised Learning — Decision Trees and Random Forests

January 24 - January 30, 2025

Explore decision trees and random forests as powerful supervised learning algorithms for classification and regression tasks.

Lecture Slides

Understanding decision tree algorithms and implementation.

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Dataset

Dataset for the quiz.

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Quiz

Practical implementation in Orange.

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04

Unsupervised Learning — Clustering and PCA

January 31 - February 6, 2025

Discover unsupervised learning techniques including clustering algorithms and principal component analysis for dimensionality reduction.

Lecture Slide

K-means, hierarchical clustering, and DBSCAN.

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Practice Exercise

Exercise with Orange for Unsupervised Learning

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Practice 2

Exercise with Orange for Unsupervised Learning.

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Dataset for Supervised Learning

Stephen Mart dataset

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05

Assessment 1

February 7 - February 13, 2025

First major assessment covering the fundamentals of AI and machine learning concepts covered in weeks 1-4.

Assessment Guidelines

Detailed instructions and requirements for Assessment 1.

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Submission Deadline

Important dates and submission requirements.

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Practice Questions

Sample questions to prepare for the assessment.

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06

Supervised Learning — SVM and Gradient Boosting

February 14 - February 20, 2025

Explore support vector machines and gradient boosting algorithms for advanced supervised learning applications.

Support Vector Machines

Understanding SVM theory and implementation.

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Gradient Boosting

XGBoost, LightGBM, and ensemble methods.

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Implementation in Orange

Practical implementation of SVM and gradient boosting.

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07

Introduction to Neural Networks

February 21 - February 27, 2025

Introduction to artificial neural networks, backpropagation, and deep learning fundamentals.

Neural Network Basics

Understanding neurons, layers, and activation functions.

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Backpropagation

Understanding gradient descent and weight updates.

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Implementation

Building neural networks from scratch.

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08

Image Classification with Deep CNNs

February 28 - March 6, 2025

Explore convolutional neural networks for image classification and computer vision applications.

Convolutional Neural Networks

Understanding CNNs and their architecture.

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Dataset

Image processing and feature extraction.

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CNN Implementation

Building asand training CNN models.

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09

Assessment 2; Bayes Classification and NLP

March 7 - March 13, 2025

Second assessment covering advanced topics including Bayesian classification and natural language processing.

ASSESSMENT PREPARATION HERE

Detailed instructions for your to recap for Assessment 2.

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Lecture Slide

Slide content

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Practice Exercise

For your assessment Preparation.

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Dataset Exercise

For your assessment Preparation.

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10

Reinforcement Learning

March 14 - March 20, 2025

Explore generative AI models including GANs, VAEs, and transformer-based generative models.

Lecture Slide

Understanding GANs and their applications.

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Dataset

Subscription Dataset

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Implementation

Building generative models in practice.

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11

Model Explanability and Interpretation

March 21 - March 27, 2025

Introduction to understand the model and how it works.

Slide Lecture

Understand Model interpretation.

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Dataset A3

Suggestions for Dataset A3.

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Quantum Implementation

Practical quantum ML examples.

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12

Quantum computing and machine learning

March 28 - April 3, 2025

Understanding how to interpret and explain AI model decisions for transparency and trust.

Lecture Slide

Techniques for understanding model decisions.

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Report Sample

Methods for explaining AI predictions.

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Implementation

Practical interpretability tools.

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Course Weeks

Introduction to Artificial Intelligence

Course Syllabus

Lecture Slides

Exercise 1

Introduction to Machine Learning

Lecture Content

Small Dataset

Orange Instruction

Practice Dataset - Retail Campaign Dataset

Dataset for Quiz - Customer Churn Dataset

Supervised Learning — Decision Trees and Random Forests

Lecture Slides

Dataset

Quiz

Unsupervised Learning — Clustering and PCA

Lecture Slide

Practice Exercise

Practice 2

Dataset for Supervised Learning

Assessment 1

Assessment Guidelines

Submission Deadline

Practice Questions

Supervised Learning — SVM and Gradient Boosting

Support Vector Machines

Gradient Boosting

Implementation in Orange

Introduction to Neural Networks

Neural Network Basics

Backpropagation

Implementation

Image Classification with Deep CNNs

Convolutional Neural Networks

Dataset

CNN Implementation

Assessment 2; Bayes Classification and NLP

ASSESSMENT PREPARATION HERE

Lecture Slide

Practice Exercise

Dataset Exercise

Reinforcement Learning

Lecture Slide

Dataset

Implementation

Model Explanability and Interpretation

Slide Lecture

Dataset A3

Quantum Implementation

Quantum computing and machine learning

Lecture Slide

Report Sample

Implementation