Machine Learning Summer School

Instructors: Kanishk Gandhi and Nishit Asnani

Lectures: 6:30 pm to 8:00 pm at KD101

Course Content

Date Topics/ Deadlines Slides/Notes
May 21 Course Overview and Math Refresher Overview Slides
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