Lecture Notes
ESM3026: Applied Statistics II (Undergraduate)
Course Introduction [download]
1. Estimation (7.1) [download]
1. Estimation (7.2) [download]
1. Estimation (7.3) [download]
1. Estimation (7.4) [download]
1. Estimation (7.5) [download]
1. Estimation (7.6) [download]
2. Sampling Distributions of Estimators (8.1-2) [download]
2. Sampling Distributions of Estimators (8.3) [download]
2. Sampling Distributions of Estimators (8.4) [download]
2. Sampling Distributions of Estimators (8.5) [download]
2. Sampling Distributions of Estimators (8.7) [download]
3. Testing Hypothesis (9.1) [download]
3. Testing Hypothesis (9.1 cont.) [download]
3. Testing Hypothesis (9.5) [download]
3. Testing Hypothesis (9.6) [download]
3. Testing Hypothesis (9.7) [download]
4. Linear Statistical Models (11.1) [download]
4. Linear Statistical Models (11.2) [download]
4. Linear Statistical Models (11.3) [download]
4. Linear Statistical Models (11.5) [download]
4. Linear Statistical Models (11.6) [download]
ESM3081: Programming for Data Science (Undergraduate)
Course Introduction [download]
1. Supervised Learning - Part 1 (Overview) [download]
1. Supervised Learning - Part 2 (k-NN) [download]
1. Supervised Learning - Part 3 (Linear Models) [download]
1. Supervised Learning - Part 4 (DT, Ensemble) [download]
1. Supervised Learning - Part 5 (SVM) [download]
1. Supervised Learning - Part 6 (ANN, Misc.) [download]
2. Unsupervised Learning - Part 1 (Overview) [download]
2. Unsupervised Learning - Part 2 (PCA) [download]
2. Unsupervised Learning - Part 3 (t-SNE) [download]
2. Unsupervised Learning - Part 4 (k-Means, Hierarchical) [download]
2. Unsupervised Learning - Part 5 (DBSCAN, Misc) [download]
3. Representing Data and Engineering Features [download]
4. Model Evaluation and Improvement [download]
5. Algorithm Chains and Pipelines [download]
6. ML Project Checklist [download]
ESM4111: Data Analytics and Machine Learning (Graduate)
* Most of the slides are adapted from Pang-Ning Tan’s materials, which are available at https://www-users.cs.umn.edu/~kumar001/dmbook
Course Introduction [download]
1. Data [download]
2. Classification: Basic Concepts [download]
3. Classification: Algorithms - Part 1 [download]
4. Classification: Algorithms - Part 2 [download]
5. Classification: Algorithms - Part 3 [download]
6. Cluster Analysis - Part 1 [download]
7. Cluster Analysis - Part 2 [download]
8. Anomaly Detection [download]
9. Association Analysis [download]
ESM5120: Learning from Data (Graduate)
Course Introduction [download]
1. Machine Learning Basics [download]
2. Deep Neural Networks [download]
3. Optimization [download]
4. Regularization [download]
5. Practical Methodology [download]
6. Convolutional Neural Networks [download]
7. Recurrent Neural Networks [download]
8. Graph Neural Networks [download]
9. Autoencoders [download]
10. Deep Generative Models [download]
11. Advanced Topics in Deep Learning [download]