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)
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)