These are the exercise files used for NICF - Pattern Recognition and Machine Learning with R course.
The course outline can be found in
https://www.tertiarycourses.com.sg/wsq-machine-learning-r.html
Topic 1 Overview of Machine Learning
- Introduction to Machine Learning
- Pattern Recognition Problems Suitable for Machine Learning
- Supervised vs Unsupervised Learnings
- Types of Machine Learning
- Machine Learning Techniques
- R Packages for Machine Learning
Topic 2 Regression
- What is Regression
- Applications of Regression
- Least Square Error Minimization
- Data Pre-processing
- Bias vs Variance Trade-off
- Regression Methods with Regularization
- Logistic Regression
Topic 3 Classification
- What is Classification
- Applications of Classification
- Classification Algorithms
- Confusion Matrix
- Classification Performance Evaluation
Topic 4 Clustering
- What is Clustering
- Applications of Clustering
- Distance Measure
- Clustering Algorithms
- Clustering Performance Evaluation
- Anomaly Detection Problem
Topic 5 Principal Component Analysis
- • Principal Component Analysis (PCA) and Dimension Reduction
- • Applications of PCA
- • PCA Workflow
Topic 6 Deep Learning
- What is Neural Network
- Activation Functions
- Loss Function Minimization
- Gradient Descent Algorithms and Learning Rate
- Deep Neural Network for Visual Recognition
- Improve Visual Recognition with Convolutional Neural Network
- The Future of AI
- AI Ethics
Mode of Assessment
- Written Assessment (Q&A)
- Practical Performance