Machine Learning

Course topics

Part 1. Supervised Learning

  • Nearest Neighbour Classifier/K-Nearest Neighbour Classifier
  • Linear Regression
  • Decision Trees
  • Overfitting
  • Train-val-test split
  • Cross-validation algorithm

Part 2. Unsupervised Learning

  • Principle component analysis
  • UMAP / t-SNE
  • K-means clustering
  • Hierarchical clustering
  • DBSCAN
  • Methods for estimating number of clusters

Part 3. Deep Learning:

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

Part 4. Regularisation

  • L1 & L2 regularisation
  • LASSO regression
  • Ridge regression
  • Weight decay
  • Dropout
  • Data Augmentation

Part 5. Ensemble methods

  • Basic ensembling (averaging, majority vote)
  • Bagging
  • Random Forest
  • Boosting
  • XGBoost
  • Stacking & Blending

Part 6. Performance metrics

  • Accuracy, Recall, precision and f1-score
  • Confusion matrix
  • ROC & AUC
  • MSE & RMSE

Prerequisites

Lecture sample