Introduction to machine learning (BA4)

Semaine	Nom	Description
Introduction to the Class	Introduction
	Python and NumPy Primer
	Matplotlib Cheatsheet
	Python Cheatsheet
	Exercise 1: Introduction to Python
	Exercise 1: Introduction to Python - SOLUTIONS
	Recorded Lecture 20/21: Introduction and kNN
	Recorded Lecture 20/21: Python and NumPy Primer
	Exercise 2: Introduction to NumPy
	Exercise 2: Introduction to NumPy - SOLUTIONS
K-Nearest Neigbors and K-Mean	K-Nearest Neighbors
	Recorded Lecture 20/21: kNN
	Exercise 3: kNN
	Exercise 3: kNN Solutions
	K-Means Clustering
	Recorded Lecture 20/21: K-Means
	Exercise 4: K-Means
	Exercise 4: K-Means Solutions
Linear Classification	Linear Classification
	Recorded Lecture 20/21: Linear Classification (1)
	Recorded Lecture 20/21: Linear Classification (2)
	Exercise Session 5: Logistic Regression
	Exercise Session 5: Logistic Regression Solution
	Max Margin Classifiers
	Recorded Lecture 20/21: Max-Margin Classifiers
	Exercise Session 6: Max Margin Classifiers
	Exercise Session 6: Max Margin Classifiers (Solution)
Non Linear Optimization	Optimization Basics
	Recorded Lecture 20/21: Optimization Basics
Non Linear Classification	AdaBoost
	Support Vector Machines
	Proof of Cover's Theorem (optional)
	Recorded Lecture 20/21: Adaboost
	Recorded Lecture 20/21: Linear SVMs
	Recorded Lecture 20/21: Kernel SVMs
	Exercise Session 7: Kernel SVM
	Exercise Session 7: Kernel SVM (solutions)
Decision Forests	Decision Forests
	Recorded Lecture 20/21: Decision Forests
Neural Networks	Multi Layer Perceptrons
	Recorded Lecture 20/21: MLPs (1)
	Recorded Lecture 20/21: MLP (2)
	Exercise Session 8 - MLP
	Exercise Session 8 - MLP Solution
	Convolutional Neural Nets
	Recorded Lecture 20/21: CNNs
	Exercise Session 9: CNN
	Exercise Session 9: CNN Solution
	Exercise Session 10: UNet
	Exercise Session 10: UNet Solutions
	Transformers
	Alpha Go
Dimensionality Reduction	Linear Dimensionality Reduction
	Exercise Session 11: PCA
	Exercise Session 11: PCA Solutions
	Non Linear Dimensionality Reduction
	Recorded Lecture 20/21: Linear Dimensionality Reduction (1)
	Recorded Lecture 20/21: Linear Dimensionality Reduction (2)
	Recorded Lecture 20/21: Nonlinear Dimensionality Reduction
	Exercise Session 12: Autoencoder
	Exercise Session 12: Autoencoder (solutions)
Applications	Shape Design (Optional)
Recap	Course Recap
	Recorded Lecture 20/21: Course Recap
Mock Exam	Mock Exam
	Mock Exam Solutions
Practice Graded Exercise - NOT THE ACTUAL ONE!	Practice Graded Exercise 1 - Fall 2020
	Practice Graded Exercise 2
Graded Exercise 1	Graded Exercise 1
	Graded Exercise 1 - Solutions
Graded Exercise 2	Graded Exercise 2
	Graded Exercise 2 - Solutions
Exam Classrooms	AAC 231
	SG 1 138