Computer Science Department
Introduction to Machine Learning and Applications CSC 5991-003 – Winter, 2015
Instructor: Dr. Dongxiao Zhu
Office Number: Maccabee 14109.3
Office Hours: 2:00pm – 3:00pm (TTH) or by appointment
Telephone Number: 313-577-3104
Email Address: (preferred way of contact)
Course Number, Section Number, and Course Title: CSC 5991-003 Special Topics in Computer Science: Intro. to Machine Learning and Applications.
Time and Place of Class Meetings: Tuesday and Thursday 4:30-5:50 p.m. in State Hall 114.
Class Website: A schedule of topics and reading assignments may be found on the Blackboard website. Please check this site often for any changes to the schedule or announcements.
Description of Course Content: Through algorithmic investigation, brainstorming, and case analysis, students develop the skills and strategies that are necessary for effective leaning from data, including Big Data emerging from science and engineering. The course format will be a combination of lecture presentation and hand-on programming sessions.
Prerequisite: CSC 2110 and CSC 3110 or equivalent.
Course Learning Outcomes:
Upon completion of this course, students will be able to do the following:
1. To understand the basic and essential concepts in machine learning.
2. To design and implement machine learning techniques for solving real- world problems.
3. To apply those techniques for data analysis.
4. To be aware of the cutting edge areas relevant to machine learning
Student Outcomes:
a. An ability to apply knowledge of computing and mathematics appropriate to the discipline
b. An ability to analyze a problem, and identify and define the computing requirements appropriate to its solution
c. An ability to design, implement and evaluate a realistic computer-based system, process, component, or program to meet desired needs
d. An ability to function effectively on teams to accomplish a common goal.
e. An understanding of professional, ethical, legal, security, and social issues and responsibilities
f. An ability to communicate effectively with a range of audiences
g. An ability to analyze the local and global impact of computing on individuals, organizations and society
h. Recognition of the need for, and an ability to engage in, continuing professional development
i. An ability to use current techniques, skills, and tools necessary for computing practices
j. An ability to apply mathematical foundations, algorithmic principles, and computer science theory in the modeling and design of computer-based systems in a way that demonstrates comprehension of the tradeoffs involved in design choices
k. An ability to apply design and development principles in the construction of software systems of varying complexity
Assessment:
Assessment of how well outcomes are being achieved will be done by applying a rubric to a random sample of at least 25% of the students who have completed the work being used for assessment. Assessment tools are quizzes, examinations/tests, lab assignments, and projects. For each outcome being assessed, each student in the sample will be judged to (a) Exemplary Competence, (b) Basic Competence (c) Approaching Competence, or (d) Beginning Competence to meet an objective standard designed to assess this outcome. The thresholds used are: Basic Competence (70%), Exemplary Competence (80%). We will say that this offering of the course achieved the particular outcome if and only if 70% or more of the students sampled were assessed to be in categories (a) or (b).
Recommended textbooks (none required):
1. Marsland, S. Machine Learning: An Algorithmic Perspective. Chapman
& Hall/Crc Machine Learning & Pattern Recognition. ISBN: 1420067184
2. Torgo, L. Data Mining with R. CRC Press. ISBN: 9781439810187
Cases: Four real-world data analysis examples. More or less, time permits.
1. Predicting algae blooms
2. Predicting stock market returns
3. Detecting fraudulent transactions
4. Personalized Medicine
Homeworks (70%): Four programming homework’s will be assigned on a regular schedule (one for every three weeks). Late homework will not be accepted (unless it is the result of an occasionally excused absence).
Term Project (30%): You will have one term project which is due at the end of the semester. You are encouraged to use machine learning techniques to solve a real-world problem in your field or interested area. Graduate students are free to choose their own topics pending my approval. Undergraduate students will work on the project that I assign.
GRADING Policy: A: 90% or above B: 80% or above C: 70% or above D: 60% or above F: below 60%
Collaboration Policy: You are encouraged to discuss and exchange ideas with other students; however, you must complete your work on your own.
Academic Dishonesty: This course will honor the University's policy of academic honesty. This policy follows. Wayne State University recognizes the principles of honesty and truth as fundamental to ethical business dealings and to a community of instructors and students. The University expects students to respect these principles. As a point of academic integrity, students are required to submit original material of their own creation. Plagiarism of any material and cheating are serious offenses and, if established with sufficient evidence, can result in failure of the course or dismissal from the University.
Tentative Schedule (subject to change)
Date / Topics / Homeworks / DueJan 13 / Introductions
Go Over Syllabus and Course
Big Data in science and engineering
Jan 15 / Programming with R
Jan 20 / Programming with R / Hwk I
Jan 22 / Intro. to supervised learning
Jan 27 / k-nn classifier
Jan 29 / Classification tree and random forest
Feb 3 / Naïve Bayes classifier
Feb 5 / Discriminative analysis (LDA) / HwkI
Feb 10 / Support vector machine
Feb 12 / Hidden Markov Model / Hwk II
Feb 17 / Intro. to unsupervised learning
Feb 19 / k-means clustering
Feb 24 / Partition Around Median (PAM)/Self- Organization Map (SOM)
Feb 26 / Hierarchical clustering / Hwk III / HwkII
March 3 / Graphical models
March 5 / Intro. to Approximate learning
March 10 / Sampling
March 12 / Gibbs sampler / Hwk IV
March 17 / Spring Break
March 19 / Spring Break
March 24 / Markov Chain Monte Carlo (MCMC) / HwkIII
March 26 / Selected Topic: Mixture models and EM
March 31 / Selected Topic: Hierarchical models
April 2 / Intro. to case studies data analysis / Project
April 7 / Predicting algae blooms / HwkIV
April 9 / Predicting stock market returns
April 14 / Detecting fraudulent transactions
April 16 / Personalized Medicine
April 21 / Project presentation & selected topics
April 23 / Project presentation & selected topics
Religious Holidays:
Because of the extraordinary variety of religious affiliations of the University student body and staff, the Academic Calendar makes no provisions for religious holidays. However, it is University policy to respect the faith and religious obligations of the individual. Students with classes or examinations that conflict with their religious observances are expected to notify their instructors well in advance so that mutually agreeable alternatives may be worked out.
Student Disabilities Services:
· If you have a documented disability that requires accommodations, you will need to register with Student Disability Services for coordination of your academic accommodations. The Student Disability Services (SDS) office is located in the Adamany Undergraduate Library. The SDS telephone number is 313-577-1851 or 313-202-4216 (Videophone use only). Once your accommodation is in place, someone can meet with you privately to discuss your special needs. Student Disability Services' mission is to assist the university in creating an accessible community where students with disabilities have an equal opportunity to fully participate in their educational experience at Wayne State University.
· Students who are registered with Student Disability Services and who are eligible for alternate testing accommodations such as extended test time and/or a distraction-reduced environment should present the required test permit to the professor at least one week in advance of the exam. Federal law requires that a student registered with SDS is entitled to the reasonable accommodations specified in the student’s accommodation letter, which might include allowing the student to take the final exam on a day different than the rest of the class.
Academic Dishonesty - Plagiarism and Cheating:
Academic misbehavior means any activity that tends to compromise the academic integrity of the institution or subvert the education process. All forms of academic misbehavior are prohibited at Wayne State University, as outlined in the Student Code of Conduct (http://www.doso.wayne.edu/student-conduct-services.html). Students who commit or assist in committing dishonest acts are subject to downgrading (to a failing grade for the test, paper, or other course-related activity in question, or for the entire course) and/or additional sanctions as described in the Student Code of Conduct.
· Cheating: Intentionally using or attempting to use, or intentionally providing or attempting to provide, unauthorized materials, information or assistance in any academic exercise. Examples include: (a) copying from another student’s test paper; (b) allowing another student to copy from a test paper; (c) using unauthorized material such as a "cheat sheet" during an exam.
· Fabrication: Intentional and unauthorized falsification of any information or citation. Examples include: (a) citation of information not taken from the source indicated; (b) listing sources in a bibliography not used in a research paper.
· Plagiarism: To take and use another’s words or ideas as one’s own. Examples include: (a) failure to use appropriate referencing when using the words or ideas of other persons; (b) altering the language, paraphrasing, omitting, rearranging, or forming new combinations of words in an attempt to make the thoughts of another appear as your own.
· Other forms of academic misbehavior include, but are not limited to: (a) unauthorized use of resources, or any attempt to limit another student’s access to educational resources, or any attempt to alter equipment so as to lead to an incorrect answer for subsequent users; (b) enlisting the assistance of a substitute in the taking of examinations; (c) violating course rules as defined in the course syllabus or other written information provided to the student; (d) selling, buying or stealing all or part of an un-administered test or answers to the test; (e) changing or altering a grade on a test or other academic grade records.
Course Drops and Withdrawals: In the first two weeks of the (full) term, students can drop this class and receive 100% tuition and course fee cancellation. After the end of the second week there is no tuition or fee cancellation. Students who wish to withdraw from the class can initiate a withdrawal request on Pipeline. You will receive a transcript notation of WP (passing), WF (failing), or WN (no graded work) at the time of withdrawal. No withdrawals can be initiated after the end of the tenth week. Students enrolled in the 10th week and beyond will receive a grade. Because withdrawing from courses may have negative academic and financial consequences, students considering course withdrawal should make sure they fully understand all the consequences before taking this step. More information on this can be found at:
http://reg.wayne.edu/pdf-policies/students.pdf
Student services:
· The Academic Success Center (1600 Undergraduate Library) assists students with content in select courses and in strengthening study skills. Visit www.success.wayne.edu for schedules and information on study skills workshops, tutoring and supplemental instruction (primarily in 1000 and 2000 level courses).
· The Writing Center is located on the 2nd floor of the Undergraduate Library and provides individual tutoring consultations free of charge. Visit http://clasweb.clas.wayne.edu/ writing to obtain information on tutors, appointments, and the type of help they can provide.
Class recordings:
Students need prior written permission from the instructor before recording any portion of this class. If permission is granted, the audio and/or video recording is to be used only for the student’s personal instructional use. Such recordings are not intended for a wider public audience, such as postings to the internet or sharing with others. Students registered with Student Disabilities Services (SDS) who wish to record class materials must present their specific accommodation to the instructor, who will subsequently comply with the request unless there is some specific reason why s/he cannot, such as discussion of confidential or protected information.