Syllabus | Slides and Assignments | Project | Instructor Course Materials Date Lecture Recording Assignment Due Date 08/29 Introduction to Data Mining Assignment0, Assignment1 09/05 08/31 Basic Python Programming 09/05 Data Science Process (Preprocessing) Assignment2 09/12 09/07 Data Science Process (Class Imbalance and Data Visualization) 09/12 Data Science Process (Evaluation) Assignment3 09/19 09/14 Data Science Process (Validation and Testing) 09/19 Supervised Learning (Naive Bayes) Assignment4 09/26 09/21 Supervised Learning (Support Vector Machine) 09/26 Supervised Learning (Nearest Neighbor) Assignment5 10/03 09/28 Supervised Learning (Artificial Neural Networks) 10/03 Supervised Learning (Decision Trees) Assignment6 10/12 10/05 Supervised Learning (Overfitting) 10/12 Supervised Learning (Ensemble Learning) Assignment7 10/19 10/17 Supervised Learning (Regression) 10/19 Unupervised Learning (K-means Clustering) Assignment8 10/26 10/24 Unupervised Learning (Hierarchical Clustering) 10/26 Optimization (Stochastic Gradient Descent) Assignment9 11/02 10/31 Optimization (Hyperparameter Tuning) 11/02 Problem Solving (Case Study) Assignment10 11/16 11/07 Project Project 11/09 Other (Semi-supervised Learning) 11/14 Other (Active Learning) 11/16 Other (Reinforcement Learning) 11/21 Other (Association Analysis) 11/28 Other (Anomaly Detection) Project Report 12/04 11/30 Other (Self-Supervised Learning) 12/05 Project Summary 12/07 Project Summary