Difference between revisions of "Machine Learning Bio"
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the final score will be the sum of HW (not compulsory) and WE scores. | the final score will be the sum of HW (not compulsory) and WE scores. | ||
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==Teaching Material (the textbook)== | ==Teaching Material (the textbook)== | ||
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Lectures: | Lectures: | ||
* [[Media:ML-2020-00-Intro.pdf | [2020] Course introduction]]: introductory slides of the course with useful information about the grading, and the course logistics. Some examples from supervised and unsupervised learning. Regression, classification, clustering terminology and examples. | * [[Media:ML-2020-00-Intro.pdf | [2020] Course introduction]]: introductory slides of the course with useful information about the grading, and the course logistics. Some examples from supervised and unsupervised learning. Regression, classification, clustering terminology and examples. | ||
| − | * [[Media:ML- | + | * [[Media:ML-2020-01-StatisticalLearning.pdf | [2020] Statistical Learning Introduction]]: Statistical Learning definition, rationale, and trade-offs (e.g., prediction vs. inference, parametric vs non parametric models, flexibility vs. interpretability, etc.) |
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* [[Media:ML-2016-03-AssessingModelAccuracy.pdf | [2016] Statistical Learning and Model Assessment]]: Model Assessment for Regression and Classification, Bias-Variance trade-off, Model complexity and overfitting, K-Nearest Neighbors Classifier vs. Bayes Classifier. | * [[Media:ML-2016-03-AssessingModelAccuracy.pdf | [2016] Statistical Learning and Model Assessment]]: Model Assessment for Regression and Classification, Bias-Variance trade-off, Model complexity and overfitting, K-Nearest Neighbors Classifier vs. Bayes Classifier. | ||
* [[Media:ML-2016-04-LinearRegression.pdf | [2016] Linear Regression]]: Simple Linear Regression and Multiple Linear Regression. Feature selection. Ridge Regression and Lasso. | * [[Media:ML-2016-04-LinearRegression.pdf | [2016] Linear Regression]]: Simple Linear Regression and Multiple Linear Regression. Feature selection. Ridge Regression and Lasso. | ||
* [[Media:ML-2016-05-LinearClassification.pdf | [2016] Linear Classification]]: From Linear Regression to Logistic Regression. Linear Discriminant Analysis and Quadratic Discriminant Analysis. Comparison between linear classification methods. | * [[Media:ML-2016-05-LinearClassification.pdf | [2016] Linear Classification]]: From Linear Regression to Logistic Regression. Linear Discriminant Analysis and Quadratic Discriminant Analysis. Comparison between linear classification methods. | ||
* [[Media:ML-2016-06-SupportVectorMachines.pdf | [2016] Support Vector Machines]]: Discriminative vs. generative methids. Hyperplanes learning and Perceptron. Maximum Margin Classifiers. The Kernel trick and Support Vector Machines. | * [[Media:ML-2016-06-SupportVectorMachines.pdf | [2016] Support Vector Machines]]: Discriminative vs. generative methids. Hyperplanes learning and Perceptron. Maximum Margin Classifiers. The Kernel trick and Support Vector Machines. | ||
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For exercises and lab material please refer to [http://davide.eynard.it/pattern-analysis-and-machine-intelligence-2015-2016/ Davide Eynard website]. | For exercises and lab material please refer to [http://davide.eynard.it/pattern-analysis-and-machine-intelligence-2015-2016/ Davide Eynard website]. | ||
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===Additional Resources=== | ===Additional Resources=== | ||
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* [http://www.onmyphd.com/?p=kkt.karush.kuhn.tucker Karush Kuhn Tucker Conditions]: a short note on their meaning with references to relevant wikipedia pages | * [http://www.onmyphd.com/?p=kkt.karush.kuhn.tucker Karush Kuhn Tucker Conditions]: a short note on their meaning with references to relevant wikipedia pages | ||
* [http://students.brown.edu/seeing-theory/ Seeing Theory]: a website where the basic concepts of probability and statistics are explained in a visual way. | * [http://students.brown.edu/seeing-theory/ Seeing Theory]: a website where the basic concepts of probability and statistics are explained in a visual way. | ||
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===Past Exams and Sample Questions=== | ===Past Exams and Sample Questions=== | ||
For some samples of exams you can check the last year PAMI ones | For some samples of exams you can check the last year PAMI ones | ||
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* [[Media:2016_09_09_PAMI.pdf |09/09/2016 PAMI Exam]] | * [[Media:2016_09_09_PAMI.pdf |09/09/2016 PAMI Exam]] | ||
* [[Media:2016_09_28_PAMI.pdf |28/09/2016 PAMI Exam]] | * [[Media:2016_09_28_PAMI.pdf |28/09/2016 PAMI Exam]] | ||
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===Online Resources=== | ===Online Resources=== | ||
The following are links to online sources which might be useful to complement the material above | The following are links to online sources which might be useful to complement the material above | ||
| + | * [https://www.r-bloggers.com/in-depth-introduction-to-machine-learning-in-15-hours-of-expert-videos/ Statistical Learning MOOC] covering the entire ISL book offered by Trevor Hastie and Rob Tibshirani. Start anytime in self-paced mode. | ||
| + | * [http://math.arizona.edu/~hzhang/math574m.html MATH 574M] University of Arizona Course on ''Statistical Machine Learning and Data Mining''; here you can find slides covering part of the course topics (the reference book for this course is again ''The Elements of Statistical Learning'') | ||
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== 2013-2014 Homework == | == 2013-2014 Homework == | ||
Revision as of 01:17, 11 March 2020
The following are last minute news you should be aware of ;-)
* 11/03/2020: The course starts today! * 03/03/2020: The course is going to start soon ...
Contents
Course Aim & Organization
The objective of this course is to give an advanced presentation, i.e., a statistical perspective, of the techniques most used in artificial intelligence and machine learning for pattern recognition, knowledge discovery, and data analysis/modeling. The course will provide the basics of Regression, Classification, and Clustering with practical exercises using the Python language.
Teachers
The course is composed by a blending of lectures and exercises by the course teacher and a teaching assistant.
- Matteo Matteucci: the course teacher
- Marco Cannici: the teaching assistant
Course Program
Techniques from machine and statistical learning are presented from a theoretical (i.e., based on statistics and information theory) and practical perspective through the descriptions of algorithms, the theory behind them, their implementation issues, and few examples from real applications. The course mostly follows the following book which is also available for download in pdf
- An Introduction to Statistical Learning with Applications in R by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
Detailed course schedule
A detailed schedule of the course can be found here; topics are just indicative while days and teachers are correct up to some last minute change (I will notify you by email). Please note that not all days we have lectures!!
Note: Lecture timetable interpretation * On Wednesday, in room ..., starts at 014:15 (cum tempore), ends at 17:15 or 18:15 * On Thursday, in room ..., starts at 08:15 (cum tempore), ends at 10:15
| Date | Day | Time | Room | Teacher | Topic |
| 11/03/2020 | Wednesday | 14:30 - 17:30 | Teams Virtual Class | Matteo Matteucci | Course Introduction (Ch. 1 ISL) |
| 12/03/2020 | Thursday | 08:15 - 10:15 | Teams Virtual Class | Matteo Matteucci | Statistical Decision Theory and Bias-Variance trade off. (Ch. 2 ISL) |
Chapters are intended as complete except for
- Ch.4 ESL: Section 4.5
- Ch.12 ESL: Sections 12.1, 12.2, 12.3
- Ch.9 ISL: Sections 9.1, 9.2, 9.3
Course Evaluation
The course evaluation is composed by two parts:
- HW: Homework with exercises covering the whole program (up to 6 points)
- WE: A written examination covering the whole program (up to 26 points)
the final score will be the sum of HW (not compulsory) and WE scores.
Teaching Material (the textbook)
Lectures will be based on material taken from the book.
- An Introduction to Statistical Learning with Applications in R by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
If you are interested in a more deep treatment of the topics you can refer to the following book from the same authors
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction. by Trevor Hastie, Robert Tibshirani, and Jerome Friedman.
Some additional material that could be used to prepare the oral examination will be provided together with the past homeworks.
Teacher Slides
In the following you can find the lecture slides used by the teacher and the teaching assistants during classes.
Lectures:
- [2020] Course introduction: introductory slides of the course with useful information about the grading, and the course logistics. Some examples from supervised and unsupervised learning. Regression, classification, clustering terminology and examples.
- [2020] Statistical Learning Introduction: Statistical Learning definition, rationale, and trade-offs (e.g., prediction vs. inference, parametric vs non parametric models, flexibility vs. interpretability, etc.)
Additional Resources
Papers and links useful to integrate the textbook
- Bias vs. Variance: "Understanding the Bias-Variance Tradeoff" essay by Scott Fortmann-Roe
- Karush Kuhn Tucker Conditions: a short note on their meaning with references to relevant wikipedia pages
- Seeing Theory: a website where the basic concepts of probability and statistics are explained in a visual way.
Online Resources
The following are links to online sources which might be useful to complement the material above
- Statistical Learning MOOC covering the entire ISL book offered by Trevor Hastie and Rob Tibshirani. Start anytime in self-paced mode.
- MATH 574M University of Arizona Course on Statistical Machine Learning and Data Mining; here you can find slides covering part of the course topics (the reference book for this course is again The Elements of Statistical Learning)
