Machine Learning

Course topics

Part 1. Supervised Learning

  • Nearest Neighbour Classifier/K-Nearest Neighbour Classifier
  • Decision Trees for Classification
  • Linear Regression
  • Decision Trees for Regression
  • Random Forest
  • Overfitting (bias-variance tradeoff)
  • Train-val-test split
  • Cross-validation algorithm

Part 2. Unsupervised Learning

  • Hierarchical clustering
  • K-means clustering
  • DBSCAN

Part 3. Dimensionality reduction

  • Principle Component Analysis
  • T-stochastic neighbor embedding (t-SNE)

Part 4. Performance evaluation

  • Accuracy
  • Problem of unbalanced classes
  • Precision and Recall
  • F1 score
  • Receiver Operating Characteristic (ROC) and Area Under the Curve (AUC)

Part 5. Deep Learning:

  • Artificial Neuron
  • Feedforward path
  • Backpropagation algorithm
  • Basics of Convolutional Neural Networks

Part 6. Applications:

  • Machine Learning in Bioinformatics, Healthcare, Neuroscience, and Commerce
  • Machine Learning and Ethics

Prerequisites

Lecture sample