BigDataAnalyticsTutorials

Tutorials for STAT4609 (2023) Big Data Analytics

Example Class 1 Intro

1. introduction to python
2. Object-Oriented Programming (OOP) in Python 3
3. PCA

Example Class 2

1. Never use 'print()' to debug again! Pysnooper is all you need!
2. Lasso Regression using scikit-learn :)
3. Application of tree-based methods with SKlearn, including, decision tree, random forest, boosting.
4. Implementation of Decision Tree from scratch

Example Class 3 Kernel Based Method

1. Kernel regression review
2. Kernel regression implementation from scratch [Simplified example for HW3]

Example Class 4

1. A1 & A2 extension: Implementation of Lasso Logistic Regression for Binary Classification from scratch

2. GAM

3. Implementation of Naive Bayes from scratch

4. Implementation of K-Means Clustering from scratch

Example Class 5 Latent Variable Models

1. Non-negative Matrix Factorization using Alternating Least Squares [Important for HW5]
2. User-based Collaborative Filtering from scratch, which is easy to extend for the item-based case.
3. User-based Collaborative Filtering using Surprise [Surprise is an easy-to-use Python scikit for recommender systems.]
4. Pytorch Introduction I

Example Class 6 Networks

1. Graph Basics with NetworkX
2. Spectral Clustering Implementation from Scratch
3. Comparison with K-means Clustering using Scikit-Learn
4. Image Segmentation using Spectral Clustering
5. Hierarchical Clustering with Scikit-Learn on both graph and tabular data

Example Class 7 NLP

1. TF-IDF:

• Implementation from Scratch
• Use SKLearn

2. Text Classification with TF-IDF

• using Naive Bayes Classifier
• using Logistic Regression

Example Class 8 SVM + Deep Learning

1. SVM implementation from scratch
2. A gentle introduction to torch.autograd.
3. Perceptron_and_neural_networks.

Example Class 9 Deep Learning

Example Class 10 Latent Dirichlet Allocation