CSC 591-023 (10722)
CSC 791-023 (11162)
Tues/Thurs 4:30 to 5:45
EE III, Room 2232 Engineering Building 3
Mid-session exam week 10 (on terminology)
No final exam.
Overview
Synopsis: “Science” is the process of communities sharing and reviewing and improving each other’s ideas. Sadly, most “data science” is not about “science”. Rather its about vendors selling you stuff that does not work properly and does not ring an alarm when it starts failing. This is strange since an important part of knowledge is “no”; i.e. the ability to critically assess something, and to recognize when one idea is better, smarter, than another. So a million million people can run data miners. But how many now when those data miners start going wrong? And how to fix faulty models? Do this subject, learn answers to those questions. Become the data scientist everyone needs to consult with when things start going wrong.
Objectives: By the end of the course, students should be able to:
- Build data miners from scratch
- Customize data miners to serve the needs of communities reviewer and improving each other models.
- Report on complex technical issues (spoken talk)
- Report on complex technical issues (written talk)
Lecturer: Tim Menzies (Prof)
- Office Hours: Tuesday, 2:00-4:30 and by request
- Location of Office Hours: EE II room 3298
- Slack name: timm
- E-Mail: tim.menzies@gmail.com
- Only use this email for private matters. All other class communication should be via the class Slack group http://fss17.slack.com.
- Phone: 304-376-2859 + Do not use this number, except in the most dire of circumstances (best way to contact me is via email).
Teaching assistant: Di Chen (Jack, Ph.D. student)
- Office Hours: Friday 8am to 10am
- Location of Office Hours: EB2 3240
- Slack name: jack_chen
Group mailing list: During term time, all communication will be via the Slack group https://fss17.slack.com. . Students are strongly encouraged to contribute their questions and answers to that shared resource.
- Note that, for communication of a more private nature, contact the lecturer on the email shown above.
Topics: Overview, methods for implementing comprehensible, fast, RAM-light, goal-aware, certification envelopers, context-aware, privacy-aware, anomaly-aware, sharable, self-tuning, incremental data mining methods.
Projecti: Students will implement and assess some of the above operators in a domain of their own choosing (ideally, some model relating to software engineering). Note that:
- CSC 791 Ph.d. student will work alone
- CSC 591 masters students will work in groups of three.
Prerequisite: Note that this is a programming-intensive subject. A programming background is required in a contemporary language such as Java or C/C++ or Python. Hence,he prerequisite for this class is 510, Software Engineering. Significant software industry experience may be substituted, at the instructor’s discretion. Students in this class will work in Python, but no background knowledge of that language will be assumed.
Suggested texts: none absolute latest version of this book.
Expected Workload: Sometimes, the lecturer/tutor will require you to attend a review session during their consultation time. There, students may be asked to review code, concepts, or comment on the structure of the course. Those sessions are mandatory and failure to attend will result in marks being deducted.
Also, this is tools-based subject and it is required that students learn and use those tools (Python, repositories, etc). Students MUST be prepared to dedicate AT LEAST 5-8 working hours a week to this class (excluding the time spent in the classroom). Laboratory instruction is not included in this subject (but the first three weeks will be spent on some in-depth programming tutorials). Note that the workload for masters and Ph.D. students will be different (see above).
Grading: The following grade scale will be used:
- A+ (97-100), A (93-96), A-(90-92)
- B+ (87-89), B (83-86), B-(80-82)
- C+ (77-79), C (73-76), C-(70-72)
- D+ (67-69), D (63-66), D-(60-62)
- F (below 60).
Grades will be added together using:
- Homeworks : 25 marks (5 homerowks 5 marks each)
- Mid-term Exam week 10 (on terminology): 15 marks
- Paper (on current directions in foundations of software science): 20 marks
- Masters students have to survey existing approaches
- Ph.D. students must (a) survey and (b) critically evaluate and (c) propose new directions
- Big project (on automated SE): 20 (essay) 10 (presentation) 10 (code review)
- The code review will be the lecturer reading the code checking that the implementation is above a minimum level of effort.
- Masters student have to motivate their design choices and demonstrate a a working system
- Ph.D. students have to (a) motivate their work and (b) demonstrate more than one working systemsand (c) critically compare and contrasts these N methods.
The mid-term exam will be on Tues Oct 17 (80 minutes long)
Homeworks and Project
Schedule:
- August will be introductory lectures.
- Semi-weekly homeworks start Aug 29 and are due Aug29, Sept12, Sept10,Sept26, Oct5.
- October is for the big project, due Nov 14
- November is for the big essay on current directions in software science, due Dec1.
- Meanwhile, all groups will present their work (each talk= 15 minites)
Homeworks:
- All deliverables are group-based (one deliverable per group)
- 500-level students: groups of three
- 700-level students: groups of one
- Homeworks will be written into a public Github repo which students will create.
- Students will shorten the url (using something like tiny.cc) of the the main file of each homework submission
- That url wull be pasted into the spreadsheet http://tiny.cc/fss17give
- Homeworks will be worth 5 marks
- Students cannot do homework i+1 till homework i gets at least 4 marks
- Homeworks can be submitted multiple times
- No late penalties
- No points taken off for repeat submissions
- ESSENTIAL: Students must submit (or resubmit) at least one item per week (or lose 2 marks)
Attendance
Attendance is extremely important for your learning experience in this class. Once you reach three unexcused absences, each additional absence will reduce your attendance grade by 10%.
Except for officially allowed reasons, your presence in the class if required from day one. Late-comers will have to work in their own solo groups (to avoid disruptions to existing groups).
Note that absences for weddings (your own, or someone else’s, is not an offically allowed reason).
Exceptions: this subject will support students who are absent for any of the following officially allowed reasons:
- Anticipated Absences (cleared with the instructor before the absence).
Examples of anticipated situations include
- representing an official university function, e.g., participating in a professional meeting, as part of a judging team, or athletic team;
- required court attendance as certified by the Clerk of Court;
- religious observances as verified by the Division of Academic and Student Affairs (DASA).
- Required military duty as certified by the student’s commanding officer.
- Unanticipated Absences. Excuses must be reported to the instructor not more than one week after the return to class. Examples of unanticipated absences are: - Short-term illness or injury affecting the ability to attend or to be productive academically while in class, or that could jeopardize the health of the individual or the health of the classmates attending. Students must notify instructors prior to the class absence, if possible, that they are temporarily unable to attend class or complete assignments on time. - Death or serious illnesses in the family when documented appropriately. An attempt to verify deaths or serious illness will be made by the Division of Academic and Student Affairs.
That support will include changing the schedule of deliverables and/or (in extreme case) different grading arrangements.
Academic Integrity
Cheating will be punished to the full extent permitted. Cheating includes plagerism of other people’s work. All students will be working on public code repositories and informed reuse is encouraged where someone else’s product is:
- Imported and clearly acknowledged (as to where it came from);
- The imported project is understood, and
- The imported project is significantly extended.
Students are encouraged to read each others code and repor uninformed reuse to the lecturer. The issue will be explored and, if uncovered, cheating will be reported to the university and marks will be deducted if the person who is doing the reuse:
- Does not acknowledge the source of the product;
- Does not exhibit comprehension of the product when asked about it;
- Does not significantly extend the product.
All students are expected to maintain traditional standards of academic integrity by giving proper credit for all work. All suspected cases of academic dishonesty will be aggressively pursued. You should be aware of the University policy on academic integrity found in the Code of Student Conduct.
The exams will be done individually. Academic integrity is important. Do not work together on the exams: cheating on either will be punished to the full extent permitted.
Disabilities
Reasonable accommodations will be made for students with verifiable disabilities. In order to take advantage of available accommodations, students must register with Disability Services for Students at 1900 Student Health Center, Campus Box 7509, 919-515-7653. For more information on NC State’s policy on working with students with disabilities, please see the Academic Accommodations for Students with Disabilities Regulation(REG 02.20.01).
Students are responsible for reviewing the PRRs which pertain to their course rights and responsibilities. These include: http://policies.ncsu.edu/policy/pol-04-25-05 (Equal Opportunity and Non-Discrimination Policy Statement), http://oied.ncsu.edu/oied/policies.php (Office for Institutional Equity and Diversity),http://policies.ncsu.edu/policy/pol-11-35-01 (Code of Student Conduct), and http://policies.ncsu.edu/regulation/reg-02-50-03 (Grades and Grade Point Average).
Non-Discrimination Policy
NC State University provides equality of opportunity in education and employment for all students and employees. Accordingly, NC State affirms its commitment to maintain a work environment for all employees and an academic environment for all students that is free from all forms of discrimination. Discrimination based on race, color, religion, creed, sex, national origin, age, disability, veteran status, or sexual orientation is a violation of state and federal law and/or NC State University policy and will not be tolerated. Harassment of any person (either in the form of quid pro quo or creation of a hostile environment) based on race, color, religion, creed, sex, national origin, age, disability, veteran status, or sexual orientation also is a violation of state and federal law and/or NC State University policy and will not be tolerated.
- Note that, as a lecturer, I am legally required to report all such acts to the campus policy.
Retaliation against any person who complains about discrimination is also prohibited. NC State’s policies and regulations covering discrimination, harassment, and retaliation may be accessed at http://policies.ncsu.edu/policy/pol-04-25-05 or http://www.ncsu.edu/equal_op/. Any person who feels that he or she has been the subject of prohibited discrimination, harassment, or retaliation should contact the Office for Equal Opportunity (OEO) at 919-515-3148.
Other Information
Non-scheduled class time for field trips or out-of-class activities are NOT required for this class. No such trips are currently planned. However, if they do happen then students are required to purchase liability insurance. For more information, see http://www2.acs.ncsu.edu/insurance/