Syllabus | Slides and Assignments | Project | Instructor Course Materials Date Lecture Recording Assignment Due Date Week 1 Tue Introduction to Data Mining Assignment0, Assignment1 Week 2 Tue Week 1 Thu Basic Python Programming Week 2 Tue Data Science Process (Preprocessing) Assignment2 Week 3 Tue Week 2 Thu Data Science Process (Class Imbalance and Data Visualization) Week 3 Tue Data Science Process (Evaluation) Assignment3 Week 4 Tue Week 3 Thu Data Science Process (Validation and Testing) Week 4 Tue Supervised Learning (Naive Bayes) Assignment4 Week 5 Tue Week 4 Thu Supervised Learning (Support Vector Machine) Week 5 Tue Supervised Learning (Nearest Neighbor) Assignment5 Week 6 Tue Week 5 Thu Supervised Learning (Artificial Neural Networks) Week 6 Tue Supervised Learning (Decision Trees) Assignment6 Week 7 Tue Week 6 Thu Supervised Learning (Overfitting) Week 7 Tue Supervised Learning (Ensemble Learning) Assignment7 Week 8 Thu Week 7 Thu Supervised Learning (Regression) Week 8 Thu Unupervised Learning (K-means Clustering) Assignment8 Week 9 Thu Week 9 Tue Unupervised Learning (Hierarchical Clustering) Week 9 Thu Optimization (Stochastic Gradient Descent) Assignment9 Week 10 Thu Week 10 Tue Optimization (Hyperparameter Tuning) Week 10 Thu Problem Solving (Case Study) Assignment10 Week 11 Thu Week 11 Tue Project Project Week 14 Sun Week 11 Thu Other (Semi-supervised Learning) Week 12 Tue Other (Active Learning) Week 12 Thu Other (Reinforcement Learning) Week 13 Tue Other (Association Analysis) Week 13 Thu Other (Anomaly Detection) Project Report Week 15 Tue Week 14 Tue Other (Self-Supervised Learning) Week 15 Tue Project Summary Week 15 Thu Project Summary
