Week 1 
Introduction 


KNearest Neighbors 


Recorded Lecture 1: Introduction and kNN 


Exercise 1: Introduction to Python 


Exercise 1: Introduction to Python  SOLUTIONS 


Python Cheatsheet 


Matplotlib Cheatsheet 


Week 2 
Recorded Lecture 2: kNN 


Recorded Lecture 2: Python 


Python and NumPy Primer 


Exercise 2: Introduction to NumPy 


Exercise 2: Introduction to NumPy  SOLUTIONS 


NumPy Cheatsheet 


Week 3 
KMeans Clustering 


Linear Classification 


Exercise 3: KNN 


Exercise 3: KNN  SOLUTIONS 


Recorded Lecture 3: KMeans 


Recorded Lecture 3: Linear Classification 


Week 4 
Linear Classification (continued) 


Recorded Lecture 4: Linear Classification (continued) 


Max Margin Classifiers 


Exercise 4: K means 


Recorded Lecture 4: MaxMargin Classifiers 


Exercise 4: K means Solutions 


Week 5 
Recorded Lecture 5: Optimization Basics 


Optimization Basics 


Exercise 5: Logistic Regression 


Exercise 5: Logistic Regression Solutions 


Week 6 
AdaBoost 


Recorded Lecture: Adaboost 


Support Vector Machines 


Recorded Lecture: SVM 


Exercise 6: Linear SVM 


Exercise 6: Linear SVM Solutions 


Week 7 
Support Vector Machines (continued) 


Recorded Lecture: Kernel SVM 


Proof of Cover's Theorem (optional) 


Exercise 7: Kernel SVM 


Exercise 7: Kernel SVM Solutions 


Week 8 
Multi Layer Perceptrons 


Recorded Lecture: MLPs 


Exercise 8: MLPs 


Exercise 8: MLPs Solutions 


Week 9 
Multi Layer Perceptrons (continued) 


Recorded Lecture: MLP continued 


Mock Exam 


Mock Exam Solutions 


Week 10 
Convolutional Neural Nets 


Exercise 9: CNNs 


Exercise 9: CNNs Solutions 


Recorded Lecture: CNNs 


Week 11 
Linear Dimensionality Reduction 


Recorded Lecture: Linear Dimensionality Reduction 


Alpha Go 


Recorded Lecture: Alpha Go 


Exercise 10: UNet 


Exercise 10: UNet Solutions 


Week 12 
Non Linear Dimensionality Reduction 


Exercise 11: Linear Dimensionality Reduction 


Exercise 11: Linear Dimensionality Reduction Solutions 


Recorded Lecture: Linear Dimensionality Reduction Part 2 


Recorded Lecture: Nonlinear Dimensionality Reduction 


Week 13 
Decision Forests 


Recorded Lecture: Decision Forests 


Shape Design 


Recorded Lecture: Shape Design 


Exercise 12: Autoencoders 


Exercise 12: Autoencoders Solutions 


Week 14 
Course Recap 


Recorded Lecture: Review Session 
