01:198:461 Machine Learning Principles (TA)
Recitation, Rutgers University, 2025
Machine Learning Principles (01:198:461, Sec. 06) surveys core ML foundations: linear/logistic regression, regularization, decision trees/forests, probabilistic models, kernels/SVMs, and an introduction to deep learning (CNNs, RNNs/LSTMs, Transformers).
Welcome! Slides and materials for the recitations will appear here.
Recitation 01 β Distributions & Hypothesis Testing (Sep 15, 2025)
- π PDF (view online): RE01-Distributions_and_Significance_Tests.pdf
- β¬οΈ PPTX (download): RE01-Distributions_and_Significance_Tests.pptx
What we covered:
- Distributions
- Continuous (Uniform, Gaussian, Studentβs t, Laplace)
- Discrete (Bernoulli, Binomial)
- Hypothesis Testing
- P-values
- ΟΒ² tests
Recitation 02 β Decision Trees & Data Generation (Sep 22, 2025)
- β¬οΈ Materials (download): HW01-Decision_Trees_and_Data_Generation.zip
- ποΈ Zoom Recording (view online): RE02-Decision_Trees_and_Data_Generation.mp4
What we covered:
- Recursive tree building
- Use a
Nodeclass to represent each node in the tree - Store necessary information such as column names, threshold, left/right children, parent, and class labels
- Use a recursive function to split the data based on the best feature at each node
- Use a
- Synthetic data generation
- Traversing the tree from a random leaf node up to the root
- Estimating feature values along the way
- Handling edge cases, such as when a leaf node has too few samples or there are missing values
Recitation 03 β K-means & Quiz-1 Review (Sep 29, 2025)
- π PDF (view online): RE03-Kmeans_and_Quiz01.pdf
- β¬οΈ PPTX (download): RE03-Kmeans_and_Quiz01.pptx
- β¬οΈ Materials (download): RE03-Kmeans_Demo.zip
- ποΈ Zoom Recording (view online): RE03-Kmeans_and_Quiz01.mp4
What we covered:
- K-Means clustering
- Step-by-step demonstration
- Code for practice and understanding
- Quiz-1 review
- Explanations for each question
- Decision tree demonstration
Recitation 04 β K-means & GMM (Oct 6, 2025)
- β¬οΈ Materials (download): HW02-Kmeans_and_GMM.zip
- ποΈ Zoom Recording (view online): RE04-Kmeans_and_GMM.mp4
What we covered:
- Implementing the K-Means
- Random and K-Means++ initialization
- Data assignment step and centroids update step
- Implementing the Gaussian Mixture Models (GMM)
- Random initialization
- E-step and M-step in Expectation-Maximization (EM) algorithm
Recitation 05 β AdaBoost & Quiz-2 Review (Oct 13, 2025)
- π PDF (view online): RE05-AdaBoost_and_Quiz02.pdf
- β¬οΈ PPTX (download): RE05-AdaBoost_and_Quiz02.pptx
- ποΈ Zoom Recording (view online): RE05-AdaBoost_and_Quiz02.mp4
What we covered:
- AdaBoost algorithm
- Step-by-step demonstration
- Quiz-2 review
- Explanations for each question
Recitation 06 β AdaBoost Stump & Regression Demo (Oct 20, 2025)
- β¬οΈ Materials (download): RE06-AdaBoost_Stump_Regression_Demo.zip
- ποΈ Zoom Recording (view online): RE06-AdaBoost_Stump_Regression_Demo.mp4
What we covered:
- AdaBoost classification with:
- Stumps (Decision Tree)
- Logistic Regression
Recitation 07 β Markov Models & Quiz-3 Review (Oct 27, 2025)
- π PDF (view online): RE07-Markov_and_Quiz03.pdf
- β¬οΈ PPTX (download): RE07-Markov_and_Quiz03.pptx
- ποΈ Zoom Recording: Sorry, the recording is missing.
What we covered:
- Markov Models
- Basic Components
- Examples in solving your assignments
- Quiz-3 review
- Explanations for each question
Office Hours
Time: Mondays 5:00 PM β 7:00 PM
Location: Computational Biomedicine Imaging and Modeling Center (Busch Campus)
Contact: daize.dong@rutgers.edu
