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This module is composed of five units. Each unit will cover a wide range of thought-provoking subject matter in addressing both theoretical and practical issues related machine learning and artificial intelligent
UNIT 1. Introduction to Machine Learning and Artificial Intelligence:
Definition of machine learning (ML) and artificial intelligence (AI)
Historical background and key milestones
Importance and applications of ML and AI in various fields
UNIT 2. Fundamentals of Machine Learning:
Supervised, unsupervised, and reinforcement learning
Training data, validation data, and test data
Feature engineering and feature selection
Evaluation metrics for ML models
UNIT 3. Regression and Classification:
Linear regression
Logistic regression
Decision trees
Random forests
Nearest neighbourhood
Unit 4. Clustering and Dimensionality Reduction:
Hierarchical clustering
Principal Component Analysis (PCA)
UNIT5. Neural Networks and Deep Learning:
Introduction to artificial neural networks (ANN)
Feedforward neural networks
Backpropagation algorithm
Convolutional Neural Networks (CNN)
Recurrent Neural Networks (RNN)
Generative Adversarial Networks (GAN)