Machine Learning Summer School

Course Content

Date	Topics/ Deadlines	Slides/Notes
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