Course layout for the semester of Spring 2023. Slides/lecture video may be available upon request.

Week # Topic Related Assignments
1 Lecture 1: Intro
Lecture 2: Data Preparation Week 1 Datacamp
2 Lecture 3: Data Preprocessing + EDA
Lecture 4: Classification, Decision Tree Week 2 Datacamp,
3 Lecture 5: Bagging, Ensemble Models, Random Forest, Boosting
Lecture 6: Classification & Evaluation Week 3 Datacamp,
Assignment-1 on EDA
4 Lecture 7: Linear Regression, Gradient Descent
Lecture 8: Gradient Descent, Polynomial Regression Week 4 Datacamp
5 Lecture 9: Polynomial Regression, Regularization, Logistic Regression
Lecture 10: Logistic Regression, Softmax Regression Week 5 Datacamp
Assignment-2 on Decision Tree, Random Forest, Boosting
6 Lecture 11: Support Vector Machine (SVM), Naive Bayes, K-nearest Neighbor (KNN)
Lecture 12: Dimensionality Reduction, Principal Component Analysis Week 6 Datacamp
7 Lecture 13: Introduction to Neural Network
Lecture 14: Week 7 Datacamp
8 Lecture 15: Review Class
Lecture 16: Midterm Midterm (March 2)
Syllabus: Materials upto Week 6
9 Lecture 17: Unsupervised Learning, Kmeans Clustering, Kmode Clustering
Lecture 18 : Hierarchical Clustering, DBSCAN Week 9 Datacamp
Assignment 3 on Logistic Regression, MLP
10 Spring Break
11 Lecture 19-20: Convolutional Neural Network, Apriori Association Rule Mining Week 11 Datacamp
12 Lecture 21-22: Sequence Modeling, Recurrent Neural Network, LSTM, Attention and Transformer Week 12 Datacamp
Assignment 4 on Unsupervised Learning
13 Lecture 23-24: Generative Modeling (Auto-encoder, Variational Auto-encoder, Generative Adversarial Network) Week 13 Datacamp
Assignment 5 on Convolutional Neural Network
14 Lecture 25-26: Reinforcement Learning
15 Lecture 27: How ChatGPT works
Lecture 28: Review
16 No more lectures Final Exam : April 27
Syllabus: everything covered after Midterm syllabus.