Assingnment 1 includes solving simple problems:
- Unsupervised Learning:
- K means Clustering with euclidean distance
- Supervised Learning:
- Linear Regression
- Batch Gradient Descent
- Mini Batch Gradient Descent
- Stochastic Gradient Descent
- Ridge Regression (L2 norm regularisation)
- Least Angle Regression(L1 norm regularisation)
- Normal equation based vesctorised implementation of regression optimisation
- Logistic Regression
- Multiclass classification using 1 vs all and 1 vs 1 algorithms
- Probabilistic Classifiers
- Likelyhood Ratio Test
- Maximum A Posteriori