The aim of this course is to develop theoretical understanding and knowledge as well as hands-on experience of applying artificial intelligence (AI) and machine learning-based tools for cybersecurity and network security. The series of theoretical lectures of module covers basic background knowledge of statistics and probability for artificial intelligence. Students will start with learning basic data analytics concepts including data visualization, data pre-processing, missing values and outlier detection, dealing with missing values and outliers in data. The common machine learning algorithms used for pattern recognition will be discussed from theoretical perspective. The module will be further directed to applying machine learning algorithms for cybersecurity. The labs will start the journey from basic Python/MATLAB programming toward applying and developing machine learning algorithms for pattern recognition in data. Publicly available dataset will be used to train and assess the performance of these algorithms. The main areas covered in this module are:• Big Data, Data Visualization and Pre-processing, Missing Values and Outlier detection• Machine Learning and Deep Learning, Supervised, Semi-supervised and Unsupervised Learning Techniques, • Regression, Classification, Clustering, o K-Nearest Neighbour (KNN), Decision Tree (DT), Bayesian Algorithm, Support Vector Machine, Neural Networks and Convolutional Neural Network (CNN)• Training and Testing Machine Learning Algorithms, Machine Learning Evaluating Parameters, Student’s T-test, Accuracy, Precision, Recall, F1-Score, and Confusion Matrix, Receiver Operating curve (ROC), Training, Validation and Testing.• Generalisation, Overfitting and Underfitting Problems, Cross Validation, Regularisation, Dropout • Network Anomaly Detection with AI, Intrusion Detection with AI, Splunk, Spam or Ham Classifier with AI, Detecting Spam email cybersecurity threats using AI• Fraud Prevention with Cloud AI, Introduction to Federated Learning• Integration of Blockchain and Machine Learning