May 21 |
Course Overview and Math Refresher |
Overview Slides
Logistics
Math Refresher
|
May 22 |
Supervised Learning - I: K-NN and Decision Trees |
Lecture Slides
Primer on Entropy
K-NN Visualization
Decision Tree Visualization
Ipython Book
Spam Data
|
May 23 |
Supervised Learning - II: Linear and Logistic Regression |
Lecture Slides
Ipython Notebook
Derivation For Linear/Ridge Regression
An intuitive explanation for Gradients
|
May 24 |
Unsupervised Learning : Clustering and Dimensionality Reduction |
Lecture Slides
Comparison of PCA and Linear Regression
K-Means Demo
|
May 25 |
Neural Networks |
Lecture Slides
Softmax Regression Notes
|
May 28 |
Neural Networks and Backprop |
Lecture Slides
Cool Visualization of Neural Nets
Backprop on Simple Neural Net
|
May 29 |
Model Fitting, Regularization and Ensembles |
Lecture Slides
Blog on Gradient Descent Variants and Optimization
|
May 30 |
Deep Learning for Images |
Lecture Slides |
May 31 |
Practical Implementations and Deep Learning Choices |
Lecture Slides
Machine Learning for NLP-1
Code Tutorial |
June 1 |
Machine Learning for Natural Language |
Machine Learning for NLP-2
Course Wrap up
Chris Olah's Blog
Karpathy's Blog
|