Course Schedule
Introduction
Clustering
- Feb 3
- Distance & Similarity - worksheet
- Feb 5
- K-means - worksheet
- Feb 10
- K-means++ - worksheet
- PROPOSAL
- Feb 12
- Hierarchical Clustering + Density Based Clustering - worksheet
- Feb 17
- NO CLASS
- Feb 18
- Density Based Clustering - worksheet
- MONDAY SCHEDULE
- Feb 19
- Soft Clustering + Clustering Aggregation - worksheet
Singular Value Decomposition
- Feb 24
- Singular Value Decomposition - worksheet
- Feb 26
- SVD cont’d - worksheet
Classification
- Mar 3
- MIDTERM 1
- Mar 5
- NO CLASS
- Mar 8 - Mar 16
- SPRING BREAK
- Mar 17
- Guest Lecture from Ge: the Development of Language Models: RNN -> LSTM -> Transformer -> ChatGPT -> DeepSeek
- Mar 19
- Intro to Classification - worksheet
- Mar 24
- Decision Trees - worksheet
- Mar 26
- Naive Bayes + Model Evaluation - worksheet
- Mar 31
- Support Vector Machines (linear/non linear) - worksheet
- MIDTERM REPORT
- Apr 2
- Recommender Systems + MIDTERM 2 LAUNCH
- Apr 7
- MIDTERM REVIEW
Regression
- Apr 9
- Linear Regression
- Apr 14
- Linear Model Evaluation
- Apr 16
- Linear Model Evaluation Cont’d
- Apr 21
- NO CLASS
- Apr 23
- Logistic Regression
- MONDAY SCHEDULE
- Apr 28
- Logistic Regression Cont’d
Neural Networks
- Apr 30
- Fundamentals of Neural Networks
- May 1
- FINAL REPORT
- Date TBD
- FINAL EXAM