Advanced Topics in Machine Learning

Data Science block 🏷️

• A1.1 - Exploratory Data Analysis
• A1.2 - Case Study on 20 newsgroup
• A1.3 - Data Science Pipeline
• A2.1 - Feature Selection (Filter Techniques)
• A2.2 - Case Study on Excess alcohol consumption among students
• A2.3 - Feature Scaling
• A2.4 - Feature Scaling on k Nearest Neighbor
• A4.1 - Data sampling techniques & strategies
• A4.2 - Model selection and evaluation (Grid Search & Cross-validation)
• A4.3 - Model comparison (using Learning curves)
• A4.4 - Statistical comparison of classifiers using Dietterich's 5x2cv paired t-test

Semi-Supervised Learning 🏷️

• A5.1 - Linear Learning Machines
• A5.2 - Dual Representation in LLM
• A5.3 - Learning decision function using LLM
• A5.4 - Support Vector Machines (SVM)
• A6.1 - Semi-Supervised Learning
• A6.2 - Propogating 1-NN
• A6.3 - Self-Training
• A6.4 - Generative Models
• A7.1 - S3VM
• A7.2 - Branch & Bound algorithm
• A7.3 - Graph-based SSL
• A7.4 - Multiview Algorithms

Constrained clustering 🏷️

• A8.1 - Instance-based & Metric-based Constrained clustering
• A8.2 - Must-link & Cannot-link constraints
• A8.3 - Constrained clustering
• A9.1 - Must-link vs. Cannot-link vs. Must-link-before
• A9.2 - COP-k-Means
• A9.3 - Constrained clustering on FCM
• A9.4 - Hierarchical constrained clustering

Markov models and KD trees 🏷️

• A10.1 - First order and n-th order Markov models
• A10.3 - K-Dimensional Trees