Course Modules
Te Postgraduate Diploma in Computer Science (AI and ML) consists of 13 modules structured so that you work systematically through the programme and build on previous knowledge as you progress.
Python Fundamentals
Control Structures
Realistic Debugging
AI/ML Foundation
MODULE 2: (5 ECTS CREDITS)
Applied Statistics
Students learn statistical techniques essential for AI and machine learning, including descriptive and inferential methods for analyzing data, designing experiments, and validating hypotheses. This module develops competencies in statistical modeling, probability distributions, and hypothesis testing—fundamental skills for training machine learning models, optimizing algorithms, and ensuring data-driven decision-making in AI applications.
Descriptive & Inferential
Probability Distributions
Hypothesis Testing
Experimental Design
MODULE 3: (5 ECTS CREDITS)
Mathematics for Computer Science
Introduces linear algebra, calculus, and probability as core mathematical foundations for AI and machine learning. Students apply vectors, matrices, eigenvalues, and Bayes’ theorem to optimize models and handle uncertainty. These concepts are essential for developing, optimizing, and interpreting machine learning algorithms, enabling students to build AI models.
Linear Algebra
Probability Theory
Differential Calculus
Matrix Operations
MODULE 4: (5 ECTS CREDITS)
Introduction to Machine Learning
Covers core ML methods (linear, logistic regression; decision trees; Random Forest), addressing missing data and class imbalance. Students learn model evaluation to build reliable industry solutions for tasks such as customer segmentation, fraud detection, or operational forecasting.
Regression Techniques
Tree-Based Methods
Missing & Imbalanced Data
Model Evaluation
MODULE 5: (5 ECTS CREDITS)
High Dimensional Data Analysis
Addresses complex datasets via dimensionality reduction (PCA, t-SNE), clustering (K-Means, DBSCAN), and feature selection (Lasso). Students learn to manage large feature spaces—essential in fields like finance, genomics, and text analytics—uncovering insights and streamlining computations.
Dimensionality Reduction
Feature Selection
Data Visualization
Scalable Processing
MODULE 6: (5 ECTS CREDITS)
Advanced Machine Learning
Explores boosting (AdaBoost, XGBoost, LightGBM) and AutoML for efficient model experimentation. Students apply SHAP and LIME for interpretability, aligning AI solutions with ethical considerations and compliance requirements in regulated industries.
Boosting Techniques
AutoML Efficiency
Model Interpretability
Ethical Compliance
MODULE 7: (5 ECTS CREDITS)
Introduction to Deep Learning
Examines neural network fundamentals (gradient descent, backpropagation) along with feedforward, convolutional, and recurrent architectures. Students learn regularization, hyperparameter tuning, transfer learning, and diffusion models for applications in image analysis, language processing, and beyond.
Neural Networks
Backpropagation Techniques
Activation Functions
Hyperparameter Tuning
MODULE 8: (5 ECTS CREDITS)
Deep Learning for Computer Vision
Focuses on convolutional networks for classification, detection, segmentation, and examines vision transformers and diffusion models for generative tasks. Students learn to handle visual data at scale, relevant to sectors like healthcare imaging, security, and autonomous systems.
Image Processing
Object Detection
Convolutional Nets
Feature Extraction
MODULE 9: (5 ECTS CREDITS)
Deep Learning for Natural Language Processing
Covers deep learning for text: embeddings (Word2Vec, GloVe), recurrent architectures (LSTM, GRU), and transformer-based models (BERT, GPT). Also explores diffusion-based language models and retrieval-augmented generation (RAG) for tasks like sentiment analysis, NER, translation, and conversational AI.
Neural Networks
Backpropagation Techniques
Activation Functions
Hyperparameter Tuning
MODULE 10: (5 ECTS CREDITS)
Productionization of Machine Learning Systems
Focuses on deploying and maintaining ML applications in professional environments. Students use containerization (Docker), API development (FastAPI, Flask), and MLOps practices for continuous integration, monitoring, and iterative improvement of AI solutions.
Continuous Integration
Continuous Integration
Version Control
Monitoring Models
MODULE 11: (5 ECTS CREDITS)
Devops
This module covers deploying high-velocity applications on distributed infrastructure using DevOps principles. Students learn Linux scripting, networking, microservices, containerization (Docker, Kubernetes), and CI/CD with Jenkins. Topics include automated testing, serverless computing, Cloud-DevOps, monitoring, and infrastructure-as-code, all with real-world examples.
Linux & Networking
Microservices & Containers =
CI/CD Pipelines
Infrastructure-as-Code
MODULE 12: (5 ECTS CREDITS)
Foundations of Cloud Computing
Covers core concepts of cloud infrastructure, including service models (IaaS, PaaS, SaaS), deployment types, and platforms like AWS, Azure, and GCP. Students gain hands-on experience with virtual machines, storage, and networking, while learning key principles of scalability, containerization, and cloud security—foundational skills for building and deploying AI and software solutions in the cloud.
Cloud Infrastructure
Service Models
Hands-On Platforms
Scalability & Security
MODULE 13: (5 ECTS CREDITS)
Advanced Applied Computer Science
This 30 credit dissertation module in the Artificial Intelligence and Machine Learning specialisation guides students through the full project life cycle—from problem definition and literature review to data exploration, modelling, and solution delivery. Projects are grounded in real-world challenges, often drawn from students’ own domains. Using appropriate AI/ML methods, students develop practical solutions and communicate their findings clearly to both technical and non-technical audiences.
Full Project Lifecycle
Real-World Challenges
AI/ML Integration
Clear Communication
Cohort-Based Learning
Join a learning community that grows together. Our cohort-based model ensures you collaborate, share experiences, and support one another throughout the program. Our dynamic, interactive approach ensures you're job-ready and confident, providing in-depth knowledge, hands-on experience, and unmatched support throughout your journey.
Flexible Schedules and Engaging Learning
Flexibility and comprehensive support
Structured schedules
interactive, hands-on learning experiences
Key Features
Flexible Schedules
Balance live, instructor-led sessions with self-paced content for an adaptable learning experience.
One-to-One Tutorials
Receive individualized guidance to address specific learning objectives or areas of interest.
Group Projects
Collaborate on authentic case studies, enhancing teamwork, problem-solving skills, and applied knowledge.
Take part in live Concept Classes to develop advanced skills and strengthen your understanding through practical coding and modeling exercises based on real-world scenarios.
Stay connected with peers and instructors through real-time updates, feedback, and collaborative tools.
Comprehensive Learning Platform
Access recorded live sessions, structured course materials, and supplementary resources at any time.
Internationally Accredited
Our Accreditation
The Data Science Institute is an accredited member of Woolf University, a recognized higher education institution in the European Union. All diploma and degree programs adhere to rigorous European Standards and Guidelines, ensuring international academic excellence and credibility.
ECTS – The Benchmark of Excellence
Our curricula are accredited through the European Credit Transfer and Accumulation System (ECTS), a recognized international standard and the world’s largest academic accreditation system. ECTS certification ensures widespread acceptance, facilitating both mobility and career development.
Why It Matters
Qualifications are internationally portable
Recognized by employers, institutions, and government agencies
Opens pathways for further academic progression and supports career development
Application and Admissions
The application process requires following the steps below, including completing an online form, receiving a conditional offer, registering with Woolf University, and finalizing admission through document verification and fee payment.
