Syllabus Information

5.1. Methodological overview

The methodology followed in this course is oriented towards achievement of the learning objectives. A wide range of teaching and learning tasks are implemented, such as

• Lectures will provide the theoretical background of artificial neural networks (ANN) and machine learning, and how these systems can be implemented in computers and electronic circuits.
• Case studies and real engineering applications of ANN will be done in the classroom, with special emphasis on intelligent environments (sensor data processing, computer vision, embedded intelligence for home appliances…).
• The students will do laboratory tasks, developing ANN applications in MATLAB by using real databases.
• Individual or group assignments (the course project).

Students are expected to participate actively in the class throughout the semester. 

5.2. Learning tasks

The course includes the following learning tasks:

Classroom activities (1.8 ECTS: 45 hours)

• T1 Course lectures (20 hours). Presentation of the fundamentals of ANN and machine learning, combining theoretical concepts with their practical applications. Course materials are available in advance at https://moodle2.unizar.es/add/
• T2 Case studies (10 hours). Different case studies will be worked out in the classroom, related to fields such as computer vision, speech recognition, quality of service of communications, home appliances with embedded intelligence, etc.
• T3 Laboratory sessions (15 hours). Five laboratory sessions will be carried out in small groups, consisting of MATLAB simulations of ANN.  Each session will be evaluated in the laboratory.

Autonomous work (3.2 ECTS: 80 hours)

• T6 Assignments (25 hours). Individual or group assignments will be proposed, in the form of a course project. The assessment criteria includes: difficulty, development, achieved results, quality of the written report and oral presentation.
• T7 Study (53 hours). Study, preparation of laboratory tasks and exam preparation. Students can also attend tutorials to solve the specific problems they might face in the course.
• T8 Assessment (2 hours). Assessment will be based on coursework (laboratory tasks and assignments) and final examination. 

5.3. Syllabus

The course will address the following topics:

• Topic 1. Fundamentals of Artificial Neural Networks and Machine Learning
• Topic 2. Unsupervised learning: SOM
• Topic 3. Supervised learning: MLP
• Topic 4. RBF, Support Vector Machines, Deep Learning and other models
• Topic 5. Electronic implementations
• Topic 6. Digital circuit implementations
• Topic 7. Application development

Laboratory sessions

1. Data preprocessing and competitive networks
2. Perceptrons: application to binary and real data
3. Hybrid neural networks and applications
4. Development of pattern classification applications
5. Development of applications related to data fitting

5.4. Course planning and calendar

Further information concerning the timetable, classroom, office hours, assessment dates and other details regarding this course, will be provided on the first day of class or please refer to the EINA website https://eina.unizar.es/ and https://moodle2.unizar.es/add/

5.5. Bibliography and recommended resources

Basic resources:

• In http://moodle2.unizar.es
• Slides (course notes) and laboratory notes.

Bibliography:

• Marsland, S. Machine Learning, CRC Press 2015
• Martín del Brío, Bonifacio. Redes neuronales y sistemas borrosos / Bonifacio Martín del Brío, Alfredo Sanz Molina ; prólogo de Lofti A. Zadeh . - 3ª ed. rev. y amp. Paracuellos de Jarama (Madrid) : RA-MA, D. L. 2006
• Haykin, S.. Neural Networks and Learning Machines / S. Haykin Pearson, 2009
• Witten, Ian H.. Data mining : practical machine learning tools and techniques / Ian H. Witten, Eibe Frank . - 2nd ed. Amsterdam [etc.] : Morgan Kaufman, cop. 2005
• Bishop, Christopher M.. Pattern recognition and machine learning / Christopher M. Bishop New York : Springer, cop. 2006
• Kohonen, Teuvo. Self-organizing maps / Teuvo Kohonen Berlin [etc] : Springer, cop. 1995
• Duda, Richard O.. Pattern classification / Richard O. Duda, Peter E. Hart, David G. Stork . - 2nd ed. New York [etc.] : John Wiley and Sons, cop. 2001