An Introduction to Machine Learning

Below, you can find the original timetable of the course “Metodi Quantitativi per l’Informatica” (Introduction to Machine Learning) which was held in 2016. In the table, you can also find the slides (in English) which I personally prepared for the course (starting from the material presented in the book of Kevin Murphy).

    

L#	Date	Topic	References	Slides
L1	4/10/2016	Introduction – Part 1	Kevin Murphy’s Book	lec1-part1
L2	6/10/2016	Introduction – Part 2	Kevin Murphy’s Book	lec1-part2
Es1	6/10/2016	MATLAB – Introduction Part 1	Getting started, Matrix operations
L3	11/10/2016	Basic Concepts	Kevin Murphy’s Book	lec2
L4	13/10/2016	Probability – Part 1	Kevin Murphy’s Book	lec3-part1
Es2	13/10/2016	MATLAB Intro Part 2, Intro GIT, k-nearest neighbor	Functions, Simple Git guide, pmtk3 K-NN
L5	18/10/2016	Probability – Part 2	Kevin Murphy’s Book	lec3-part2
L6	20/10/2016	Probability – Part 3	Kevin Murphy’s Book	lec3-part3
Es3	20/10/2016	Exercises on KNN: use and comparison of train/validation set, CV, invariance to the permutations of the features	Kevin Murphy’s Book
L7	25/10/2016	Generative Models for Discrete Data – Part 1	Kevin Murphy’s Book	lec4-part1
L8	27/10/2016	Generative Models for Discrete Data – Part 2 (1/2)	Kevin Murphy’s Book	lec4-part2
Es4	27/10/2016	MATLAB exercises on Linear, Polynomial, and Logistic Regression	Kevin Murphy’s Book
L9	03/11/2016	Generative Models for Discrete Data – Part 2 (2/2)	Kevin Murphy’s Book	lec4-part2
Es5	03/11/2016	Exercise on Dirichlet-Multinomial Posterior Predictive, review of KNN exercise using FLANN	Kevin Murphy’s Book, FLANN
L10	08/11/2016	Generative Models for Discrete Data – Part 3 (1/3)	Kevin Murphy’s Book	lec4-part3
L11	10/11/2016	Generative Models for Discrete Data – Part 3 (2/3)	Kevin Murphy’s Book	lec4-part3
Es6	10/11/2016	Naive Bayes classifier applied to text data (bag of words): Train, visualize class conditional densities and top N words (for both datasets)	Kevin Murphy’s Book
L12	15/11/2016	Generative Models for Discrete Data – Part 3 (3/3)	Kevin Murphy’s Book	lec4-part3
L13	17/11/2016	Gaussian Models – Part 1	Kevin Murphy’s Book	lec5-part1
Es7	17/11/2016	Feature selection using mutual information. Bag of words exercises with feature selection.	Kevin Murphy’s Book
L14	22/11/2016	Gaussian Models – Part 2 (1/2)	Kevin Murphy’s Book	lec5-part2
L15	24/11/2016	Gaussian Models – Part 2 (2/2)	Kevin Murphy’s Book	lec5-part2
Es8	24/11/2016	Exercise on Naive Bayes Posterior Predictive	Kevin Murphy’s Book
L16	28/11/2016	Linear Regression – Part 1	Kevin Murphy’s Book	lec6
L17	1/12/2016	Linear Regression – Part 2	Kevin Murphy’s Book	lec6
Es9	1/12/2016	Gaussian Ellipses and Ellipsoids	Kevin Murphy’s Book
L18	6/12/2016	Logistic Regression	Kevin Murphy’s Book	lec7
L19	13/12/2016	Principal Component Analysis	Kevin Murphy’s Book	lec8
L20	15/12/2016	Kernel Methods	Kevin Murphy’s Book	lec9
Es10	15/12/2016	Support Vector Machine (SVM): A Practical Guide	A Practical Guide to Support Vector Classification
L21	20/12/2016	Gaussian Processes	Kevin Murphy’s Book	lec10