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. Deep Learning:

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

Part 4. Regularisation

  • Loss function
  • Binary cross-entropy error
  • Mean Squared Error
  • Residual sum of squares

Part 5. Ensemble methods

  • Multiple models

Part 6. Machine Learning applications

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

Prerequisites

Lecture sample