Difference between revisions of "SC:Soft Computing"

Revision as of 22:52, 5 September 2011

The following are last minute news you should be aware of ;-)

 06/10/2011: the Soft Computing course starts today!

Course Aim & Organization

Soft Computing includes technologies (Fuzzy Systems, Neural Networks, Stochastic Algorithms and models) to model complex systems and offers a powerful modeling tool for engineers and in general people needing to model phenomena. Among the application areas, we mention: data analysis, automatic control, modeling of artificial and natural phenomena, modeling of behaviors (e.g., of users and devices), decision support.

The course will introduce rigorously the fundamentals of the different modeling approaches, will put in evidence the application possibilities, by comparing different models, examples and application cases, will introduce design techniques for systems based on these technologies.

Teachers

The course is composed by a blending of lectures and exercises by the course teacher and the teaching assistant:

• Andrea Bonarini: the teacher
• Matteo Matteucci: the teaching assistant

Course Program

• What is Soft Computing: fuzzy systems, neural networks, stochastic algorithms and models;
• Fuzzy models: fuzzy sets, fuzzy logic, fuzzy rules, motivations for fuzzy modeling;
• Neural networks: basics, supervised and unsuprvised learning, main modelsi, selection and evaluation;
• Stochastic models: basics, optimization of models, fitness function, model definition, genetic algorithms, reinforcement learning, bayesian networks;
• Hybridization: motivations, neuro-fuzzy systems, genetic algoritms to optimize neural networks and fuzzy systems;
• Applications: motivations, choices, models, case studies.

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 (they will be notified to you by email).

Course Evaluation

The exam is a test done in regular sessions, starting from the end of the lessons. The test is partitioned in two parts, whose evaluation is averaged. For each of them 32 points are available and a minimum of 15 is needed. The average vote must be greater or equal to 18 to pass the exam. Some example from past years are available below. From year 2011 the format of the exam will change a little bit, but the type of questions will analogous. An example of the format for this year will be published later.

This course can be taken as a stand alone course or as a course integrated with Artificial Intelligence. In both cases, the course will be offered at the same time to students taking one or the other format. The exam will be also the same, but, in the case of integrated course, it will have to be passed together with the exam of Artificial Intelligence, as a unique exam, the same day. The same rules apply for the exam of the integrated course, and the marks obtained in SC and AI will be averaged. The difference between the two solutions is that the integrated course can be selected as a unique course in the study plan.

Teaching Material (the textbook)

Right now, the official course website is maintained by Andrea Bonarini at [1]

Lectures will be based on material taken from the aforementioned slides and from the following book.

• 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:

• Course introduction: introductory slides of the course with useful information about the grading, and the course logistics. Some examples from supervised learning and two algorithms for classification (taken from The Elements of Statistical Learning book).
• Linear Classification Examples: slides presenting images, tables and examples about (generalized) linear methods for classification (taken from The Elements of Statistical Learning book).
• Kernel Smoothing Examples: slides presenting images, tables and examples about Kernel Smoothing, Kernel Density Estimation and Gaussian Mixture Models (taken from The Elements of Statistical Learning book).
• Decision Trees and Classification Rules: these slides have been used to present decision trees and decision rules complementing the material in Ch. 9.2 of the The Elements of Statistical Learning book.
• Support Vector Machines: these slides have been used to present Support Vector Machines (taken from The Elements of Statistical Learning book).

Additional Papers

Papers used to integrate the textbook

• Bradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani, Least Angle Regression Annals of Statistics (with discussion) (2004) 32(2), 407-499.
• Burges, Christopher J. C., 1998. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2(2), 121–167.
• ...

Clustering Slides

These are the slides used to present clustering algorithms during lectures

• Lesson 1: Introduction to Clustering and K-Means (slides, handouts)

• Lesson 2: K-Means alternatives, Hierarchical, SOM (slides, handouts)

• Lesson 3: Mixture of Gaussians, DBSCAN, Jarvis-Patrick (slides, handouts)

• Lesson 4: Evaluation measures (slides, handouts)

Past Exams and Sample Questions

These are the text of past exams to give and idea on what to expect during the class exam:

• 29/06/2011 Exam
• ...

Exam Results

From time to time, you can find here results for the Soft Computing exams, please refer to the official course website for up to date news:

• Follow this link to the results of 14/07/2011 exam.

2011 Homework

Here you can find the homework for the year 2011 and the material you need to complete it. Please read the F.A.Q. below and for any unsolved doubt contact the teachers of the course.

• Homework 2011 v02 a minor change in the signature of the logistic regression function
• Homework 2011 v01 text with questions and exercises
• Dataset for the clustering exercise in csv format

Frequently Asked Questions

• How do I take the square root of a matrix?: check the diagonalization approach from [2].

• How do I compute the chi square statistics?': in the slide there is a cut and paste error since e_ij=R_it*C_tj as described here [3]

• When it is due? In which format?: The homework is due on the 29/06 and should be delivered by email. Send us (all the course teachers) the .m files in a zip archive attached to this email and a link to the pdf with the written part (not to flood our mailboxes).

• Can we do that in groups? How many people per group?: Yes, you can work on the homework in groups, but no more than 3 people per group are allowed. Put the names of all homework authors in the pdf and in all the .m files. If you discuss something with other people, w.r.t. the people in your group, point it out in the pdf file as well.

• Can we ask questions about the exercises or the code?: Yes you should! First of all, there might be unclear things in the exercise descriptions and those should be clarified as soon as possible for all (this is why the homework is versioned). But you could ask for help as well, our goal is to have you all solving all the questions and get a high grade ... but we will not do the homework on you behalf ;-)

• How the optional questions are graded?: They compensate for possible errors in the other questions; we suggest to work on them anyway to be sure you get the maximum grading.

• How the homework will be graded?: we are interested in understanding if you understood or not; thus we are not interested in the result, but we want to check how you get to the result. So please: 1) clarify all the assumptions and all the steps in your exercises 2) comment as much as possible your .m files!