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 my TA recitations will appear here.
Recitation 01 — Distributions & Hypothesis Testing (Sep 15, 2025)
- 📄 PDF (view online): RE01-Distributions_and_Significance_Tests.pdf
- ⬇️ PPTX (download): RE01-Distributions_and_Significance_Tests.pptx
What we covered
- Distributions
- Continuous (Uniform, Gaussian, Student’s t, Laplace)
- Discrete (Bernoulli, Binomial)
- Hypothesis Testing
- P-values
- χ² tests
Office Hours
Time: Not yet scheduled
Location: Not yet scheduled
Contact: daize.dong@rutgers.edu