Master of Artificial Intelligence and Machine Learning

This module explores advanced mathematical foundations essential to artificial intelligence and machine learning, including linear algebra, calculus, and probability theory. It establishes a strong conceptual base for understanding how machine learning algorithms function and are developed. Students engage in statistical analysis techniques, learning how to interpret data and conduct hypothesis testing—key skills for informed decision-making in AI-driven projects.

The module emphasizes the development of solid quantitative and analytical capabilities, enabling learners to apply mathematical and statistical reasoning to the design, evaluation, and optimization of AI models. Through a blend of theoretical instruction and practical exercises, students gain the confidence to navigate the mathematical frameworks that underpin intelligent systems, leading to a deeper understanding of the algorithms driving modern AI applications.

Learning Outcomes

Upon completion of this module, students will be able to:

• Explain the functionality and application of Python libraries such as NumPy, pandas, and matplotlib for data processing and analysis.

• Apply descriptive and inferential statistical techniques to analyze and interpret datasets.

• Formulate and test statistical hypotheses to support data-driven decision-making.

• Utilize probability theory and Bayes’ theorem to assess uncertainty and update probabilities based on empirical evidence in data science contexts.

Content Covered

• Numerical computation using NumPy

• Data manipulation and analysis with pandas

• Data visualization with matplotlib

• Descriptive and inferential statistics

• Hypothesis formulation and testing

• Applications of probability theory

• Bayes’ theorem and its applications

• Correlation and regression analysis

This module offers an in-depth study of Python as a versatile and powerful programming language within the artificial intelligence and machine learning ecosystem. Students develop practical proficiency in key Python libraries such as NumPy, pandas, matplotlib, and scikit-learn, enabling them to perform data manipulation, analysis, visualization, and the implementation of machine learning algorithms. Through hands-on coding exercises and applied projects, learners gain the skills to preprocess and transform data, develop predictive models, and evaluate their performance.

A strong emphasis is placed on data cleaning and preprocessing, which are critical stages in the data mining and machine learning pipeline. Students learn how to identify and resolve data quality issues, including missing values, duplicates, and inconsistencies, and how to convert raw data into structured formats suitable for analysis. The module also introduces exploratory data analysis (EDA) and pattern discovery techniques, equipping students to manage both numerical and textual datasets effectively. By the end of the module, learners will understand Python’s central role in AI development and be prepared to contribute confidently to machine learning projects in both academic and professional environments.

Learning Outcomes

Upon successful completion of this module, students will be able to:

• Explain the importance of exploratory data analysis (EDA) and the processes involved in data cleaning and preprocessing.

• Apply a range of data preprocessing techniques to address data quality challenges effectively.

• Demonstrate competency in multiple data cleaning and preprocessing methods.

• Manage and preprocess both numerical and text-based datasets using Python.

Content Covered

• Hands-on practice with the NumPy library

• Hands-on practice with the pandas library

• Hands-on practice with the matplotlib library

• Introduction to the scikit-learn library

• Data collection techniques

• Numerical data cleaning and preprocessing

• Text data cleaning and preprocessing

• Applied dataset-based exercises

• Pattern discovery techniques

• Clustering and classification methods

This module offers a comprehensive introduction to core artificial intelligence and machine learning methods that enable systems to learn from data without explicit programming. It explores key areas of artificial intelligence, with a particular focus on machine learning, and highlights their real-world applications across various domains. Students develop a solid foundation in essential AI concepts and terminology, examine contemporary challenges and ethical considerations related to AI, and gain insights from industry experts on learning pathways and career development in the field.

Building on data preprocessing concepts, the module progresses to a study of fundamental machine learning algorithms used in both supervised and unsupervised learning. Learners explore techniques such as linear regression, k-nearest neighbors (k-NN), decision trees, random forests, and logistic regression, along with model training, testing, and performance evaluation.

Learning Outcomes

Upon completion of this module, students will be able to:

• Demonstrate an understanding of fundamental concepts in artificial intelligence and machine learning.

• Analyze datasets and apply appropriate preprocessing techniques for machine learning tasks.

• Explain the principles of supervised and unsupervised learning algorithms.

• Critically evaluate established machine learning approaches, including the mathematical foundations underlying key algorithms.

Content Covered

• Introduction to machine learning

• Supervised machine learning techniques

• Unsupervised machine learning techniques

• Regression analysis

  • Linear regression

  • Univariate and multivariate models

• Algorithm selection strategies

• Decision trees and random forests

• Logistic regression

• Model training and testing

• Model evaluation using accuracy, precision, recall, and F1-score

This module examines advanced mathematical foundations and discrete optimization techniques essential for developing robust and high-performance machine learning systems. Students apply Python-based multivariate calculus to machine learning problems, gaining insight into how mathematical intuition supports the development of natural language processing (NLP) algorithms. Through practical demonstrations, learners explore the use of calculus concepts—such as limits and series expansions—implemented using Python to support algorithmic design and optimization.

The module places strong emphasis on natural language processing, introducing Python libraries such as the Natural Language Toolkit (NLTK) for efficient text processing tasks, including tokenization, parsing, named entity recognition, lemmatization, and semantic analysis. Students examine techniques for extracting linguistic features such as synonyms and antonyms, and gain hands-on experience in text analysis for machine learning applications.

In addition, the module explores the relationship between machine learning and NLP through Python-based algorithm implementation. It introduces foundational neural network architectures—including feed-forward neural networks, recurrent neural networks (RNNs), and convolutional neural networks (CNNs)—and applies them to NLP tasks such as utterance classification and sequence tagging.

Learning Outcomes

Upon successful completion of this module, students will be able to:

• Apply prerequisite Python skills for natural language processing applications.

• Utilize Python-based NLP libraries to develop scripts for text preprocessing and analysis.

• Explain methods for text representation, vectorization, and feature extraction in machine learning use cases.

• Design and implement automated NLP systems, including speech recognition, speech-to-text, and text-to-speech applications.

Content Covered

• Introduction to machine learning

• Supervised and unsupervised learning

• Regression and classification techniques

• k-Nearest Neighbors (KNN)

• Support Vector Machines (SVM)

• Decision theory

• Keras and TensorFlow frameworks

• Machine learning deployment

• Text analysis applications

• Automated speech recognition

• Speech-to-text and text-to-speech conversion

• Feed-forward neural networks

• Recurrent neural networks (RNNs)

• Convolutional neural networks (CNNs)

• Utterance classification

• Sequence tagging

This module introduces students to image processing and computer vision using Python, beginning with numerical computation through the NumPy library and visual data handling using OpenCV. Learners gain hands-on experience in reading, modifying, and analyzing images and videos, including real-time video streaming from webcams. The module covers essential image processing techniques such as color mapping, blending, thresholding, and gradient analysis, providing a solid foundation in visual data manipulation.

Students are introduced to modern deep learning frameworks and network architectures, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), and explore their applications in computer vision tasks. The module demonstrates how TensorFlow and Keras can be used to develop and deploy deep learning–based computer vision solutions. Learners apply these techniques to enable computers to interpret and understand digital images and videos, including object and pattern recognition.

The module also explores the use of machine learning approaches—such as neural networks, k-means clustering, and support vector machines—for supervised, unsupervised, and semi-supervised computer vision applications. Advanced topics include object detection using state-of-the-art models such as Faster R-CNN (Inception V2) and YOLO, along with the use of the Anaconda development environment for building and testing computer vision systems.

Learning Outcomes

Upon successful completion of this module, students will be able to:

• Explain core deep learning concepts and construct artificial neural networks across multiple layers of data abstraction.

• Design and implement automated computer vision algorithms using YOLO.

• Apply deep learning models such as CNNs and RNNs to image and video analysis tasks.

• Evaluate recent advancements in computer vision, artificial intelligence, and machine learning techniques.

Content Covered

• Introduction to machine learning

• Regression techniques

• k-Nearest Neighbors (KNN)

• Support Vector Machines (SVM)

• Deep learning fundamentals

• Keras and TensorFlow frameworks

• Introduction to computer vision

• Neural network models

• Convolutional Neural Networks (CNNs)

• Recurrent Neural Networks (RNNs)

• Model lifecycle management

• Image data manipulation using the Pillow Python library

• Conversion of images to and from NumPy arrays

The Industry-Based Capstone Project in the Master of Artificial Intelligence and Machine Learning is designed to provide students with hands-on experience through close collaboration with industry mentors. This partnership offers real-world exposure and practical insight into industry-specific challenges that AI and machine learning solutions are intended to address. Throughout the project, students learn to identify business problems, collect and analyze relevant data, apply appropriate AI and ML algorithms, and evaluate the effectiveness of their solutions.

The module equips learners with established best practices and methodological frameworks for managing end-to-end project development, prioritizing challenges, and delivering impactful solutions. It also strengthens essential professional competencies, including project management, time management, problem-solving, and presentation skills. Upon successful completion of the capstone project, students will have developed strong capabilities in applied data analysis, solution design, stakeholder communication, and practical implementation. In addition, the project provides valuable industry exposure and networking opportunities, enhancing graduates’ employability and competitive advantage in the job market.

Learning Outcomes

Upon completion of this module, students will be able to:

• Apply artificial intelligence and machine learning methodologies to solve complex, industry-specific problems.

• Design, develop, and implement end-to-end AI and ML solutions.

• Collaborate effectively with business and industry stakeholders.

• Evaluate and analyze the performance and impact of AI-driven solutions.

Content Covered

• Development and implementation of complete AI and ML systems within a chosen industry domain

• Professional communication and collaboration with business stakeholders

• Identification and analysis of business needs and requirements

• Proposal and justification of AI-driven solutions

• Performance evaluation of AI and ML models and assessment of their industry impact

• Measurement of solution effectiveness and identification of areas for future improvement

Capstone Project Areas

Capstone projects may be undertaken across a wide range of industries, including but not limited to:

Healthcare

• Predicting disease risk (e.g., cancer, cardiovascular conditions)

• Identifying patients likely to respond to specific treatments

• Enhancing diagnostic accuracy using machine learning models

• Developing data visualization tools to identify patient trends and patterns

Finance

• Fraud detection and transaction monitoring

• Stock market prediction and financial forecasting

• Loan risk assessment using machine learning

• Financial data visualization for trend analysis

Retail

• Personalized product recommendation systems

• Demand forecasting and inventory optimization

• Supply chain optimization using machine learning

• Customer behavior analysis through data visualization

Manufacturing

• Defect detection and quality control automation

• Predictive maintenance and machine failure forecasting

• Process optimization through machine learning

• Production data visualization and performance analysis

Technology

• Improving search engine accuracy

• Recommendation systems for digital content

• Performance optimization of machine learning models

• Visualization of complex datasets for enhanced user understanding