Course: ISYE 6470(Prof. Yao Xie), GT, Summer 2023

0. Introduction to ML
   
1. Clustering
   
   • K-means
     
2. Spectral Clustering
   
3. Dimensionality Reduction
   
   • Principal Component Analysis (PCA)
     
   • Singular Value Decomposition (SVD)
     
   • Latent Semantic Analysis (LSA)
     
4. Non-linear Dimensionality Reduction
   
   • ISOMAP with Multi-dimensional scaling (MDS) algorithm
     
   • t-SNE
     
5. Density Estimation
   
6. Gaussian Mixture Model and EM Algorithm
   
7. Basic Optimization
   

1. matrix cookbook
2. The Elements of Statistical Learning: Data Mining, Inference, and Prediction
   • https://hastie.su.domains/ElemStatLearn/
3. Foundations of Machine Learning
   • https://cs.nyu.edu/~mohri/mlbook/
4. Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares
   • PDF, Lecture slides, additional exercises, and Python & Julia Language companions
   • https://web.stanford.edu/~boyd/vmls/
   • Lecture video: https://www.youtube.com/playlist?list=PLoROMvodv4rMz-WbFQtNUsUElIh2cPmN9

Plan to upload
Classification
SVM
Neural Networks
Feature selection
Anomaly detection
Boosting algorithms
Regression and random forest
Bias-Variance Tradeoff and Cross-Validation
Kernel Methods
Reinforcement learning
Final_review