Introduction to machine learning (BA4)

Semaine	Nom	Description
Week 1	Introduction
	K-Nearest 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	K-Means Clustering
	Linear Classification
	Exercise 3: KNN
	Exercise 3: KNN - SOLUTIONS
	Recorded Lecture 3: K-Means
	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: Max-Margin 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: U-Net
	Exercise 10: U-Net 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