ࡱ> _ B'bjbj .h1bh1b  8D:~~~~& ,......$=XRR~~4g###~~,#,##tt~pYUr^}0K ttK4$<0"#&:RR!f B ^:  1. Course title/number, number of credit hours Introduction to Deep Learning CAP 4613 3 credit hours 2. Course prerequisites, corequisites, and where the course fits in the program of study Prerequisites: COP3530 Data Structures and algorithm analysis, with minimum grade of C 3. Course logistics Term: Fall 2019 Class location and time: TBD 4. Instructor contact information Instructors name Office address Office Hours Contact telephone number Email addressDr. Xingquan Zhu Engineering East (EE-96) Bldg., Room 509 TBD 561-297-3452 xzhu3@fau.edu 5. TA contact information TAs name Office address Office Hours Contact telephone number Email addressN/A N/A N/A N/A N/A6. Course description  This course teaches students basic concepts of deep learning. The class will cover three major topics including statistical machine learning, neural network structures, and deep neural networks. Detailed topics include introduction to machine learning algorithms, perceptron learning, and multi-layer neural networks, and deep neural network structures and learning algorithms. The lectures will include practical sessions dedicated to the implementation and programming of deep learning framework. 7. Course objectives/student learning outcomes/program outcomes Course objectives The goal of this class is for students to gain hands-on experiences on deep learning and its applications to numerous domains. At the end of the class, students should be able to understand the whole process of building deep learning framework. We will use R as the programming language and teach students how to implement deep learning modules for object recognition, classification, etc. 8. Course evaluation method Home Work - 40% Midterm - 15% Term Project - 20% Final - 25%9. Course grading scale Grading Scale: 90 and above: A, 85-89: A-, 76-84: B+, 70-75: B, 66-74 : C+, 60-65: C, 50-59: D, 49 and below: F. 10. Policy on makeup tests, late work, and incompletes Makeup tests are possible, and are given only if there is solid evidence of medical or otherwise family/personal emergency issues that prevent the student from participating in the exam. Makeup exam should be administered and proctored by department personnel unless there are other pre-approved arrangements Late work is not acceptable. A grade of incomplete will be assigned only in the case of solid evidence of medical or otherwise serious emergency situation. 11. Special course requirements N/A12. Classroom etiquette policy University policy requires that in order to enhance and maintain a productive atmosphere for education, personal communication devices, such as cellular phones and laptops, are to be disabled in class sessions. 13. Attendance policy statement Students are expected to attend all of their scheduled University classes and to satisfy all academic objectives as outlined by the instructor. The effect of absences upon grades is determined by the instructor, and the University reserves the right to deal at any time with individual cases of non- attendance. Students are responsible for arranging to make up work missed because of legitimate class absence, such as illness, family emergencies, military obligation, court-imposed legal obligations or participation in University-approved activities. Examples of University-approved reasons for absences include participating on an athletic or scholastic team, musical and theatrical performances and debate activities. It is the students responsibility to give the instructor notice prior to any anticipated absences and within a reasonable amount of time after an unanticipated absence, ordinarily by the next scheduled class meeting. Instructors must allow each student who is absent for a University-approved reason the opportunity to make up work missed without any reduction in the students final course grade as a direct result of such absence.14. Disability policy statement In compliance with the Americans with Disabilities Act Amendments Act (ADAAA), students who require reasonable accommodations due to a disability to properly execute coursework must register with Student Accessibility Services (SAS) and follow all SAS procedures. SAS has offices across three of FAUs campuses Boca Raton, Davie and Jupiter however disability services are available for students on all campuses. For more information, please visit the SAS website at  HYPERLINK "http://www.fau.edu/sas/" www.fau.edu/sas/ 15. Counseling and Psychological Services (CAPS) Center Life as a university student can be challenging physically, mentally and emotionally. Students who find stress negatively affecting their ability to achieve academic or personal goals may wish to consider utilizing FAUs Counseling and Psychological Services (CAPS) Center. CAPS provides 鶹ƷƵstudents a range of services individual counseling, support meetings, and psychiatric services, to name a few offered to help improve and maintain emotional well-being. For more information, go to  HYPERLINK "http://www.fau.edu/counseling/" http://www.fau.edu/counseling/ 16. Code of Academic Integrity Policy Statement Students at 鶹ƷƵ are expected to maintain the highest ethical standards. Academic dishonesty is considered a serious breach of these ethical standards, because it interferes with the university mission to provide a high quality education in which no student enjoys an unfair advantage over any other. Academic dishonesty is also destructive of the university community, which is grounded in a system of mutual trust and places high value on personal integrity and individual responsibility. Harsh penalties are associated with academic dishonesty. For more information, see  HYPERLINK "http://www.fau.edu/regulations/chapter4/Reg_4.001_5-26-10_FINAL.pdf" University Regulation 4.001. 17. Required texts/reading  Deep Learning with R, Franois Chollet with J. J. Allaire, ISBN 9781617295546, January 2018 18. Supplementary/recommended readings  Neural Networks for Pattern Recognition , Christopher M. Bishop, Clarendon Press, 1996 (Online version available) Deep Learning , Ian Goodfellow, Yoshua Bengio, and Aaron Courville, The MIT Press, 201619. Course topical outline, including dates for exams/quizzes, papers, completion of reading Weekly course topics Weekly schedule Topic Week 1 Introduction to neural networks and R programming Week 2 R programming basics (homework 1) Week 3 Perceptron learning Week 4 Multi-Layer Neural Networks Week 5 Backpropagation Learning (homework 2) Week 6 R Programming for Neural Networks Week 7 Deep Learning Neural Network Structures (midterm) Week 8 Convolutional Neural Networks (CNN) Week 9 R Programming for CNN (homework 3) Week 10 CNN for Image Recognition Week 11 Auto-Decoder (homework 4) Week 12 Auto-Decoder for Fraud Detection Week 13 Word Embedding Learning Week 14 Word Embedding Learning for Document Classification Week 15 Final Report (term project report) Project: The goal of the term project is to practice knowledge learned from the class and have each student to work on a hands on project during the second part of the class. Each student is required to identify a suitable topic (such as image recognition or text classification), and apply deep learning skills learned from the class to solve a research problem, implement and validate the design, and collect experimental results for reporting. Students will prepare a minimum 4-page technical report, and present their work in the class.      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