Difference between revisions of "CSCI4155/CSCI6505 (2016)"
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=== Course Description === | === Course Description === | ||
| − | This course is an introduction to machine learning, including their practical use and theoretical foundation. This year we will emphasize deep learning and will be using Python with advanced implementations such as Googles Tensorflow | + | This course is an introduction to machine learning, including their practical use and theoretical foundation. This year we will emphasize deep learning and will be using Python with advanced implementations such as SKLearn and TFLearn (Googles Tensorflow). We will start by showing how to apply pre-programmed algorithms in Python to get some practical experience before unpacking some of the theory behind them. The course includes introductory reviews of scientific programming with Python. The course requires basic knowledge of mathematical concepts such as calculus and linear algebra as well as the formalism of describing uncertainty with probability theory. |
=== Course Textbook === | === Course Textbook === | ||
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=== Grading Scheme === | === Grading Scheme === | ||
| − | + | Assignments 50%, Midterm 20%, Final 30%. | |
| − | + | Some of the assignment are different between the ugrad and grad version. | |
Some of the assignments may be group work, but you have to pass all individual components in order to pass the course. | Some of the assignments may be group work, but you have to pass all individual components in order to pass the course. | ||
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=== Schedule === | === Schedule === | ||
Revision as of 20:08, 31 May 2016
Contents
Machine Learning 2016
Instructors
Prof: Dr. Thomas Trappenberg (tt@cs.dal.ca)
Office: Room 4216 in Mona Campbell Building
TA: Paul Hollensen (paulhollensen@gmail.com)
Office hours: By appointment (write email)
Course Description
This course is an introduction to machine learning, including their practical use and theoretical foundation. This year we will emphasize deep learning and will be using Python with advanced implementations such as SKLearn and TFLearn (Googles Tensorflow). We will start by showing how to apply pre-programmed algorithms in Python to get some practical experience before unpacking some of the theory behind them. The course includes introductory reviews of scientific programming with Python. The course requires basic knowledge of mathematical concepts such as calculus and linear algebra as well as the formalism of describing uncertainty with probability theory.
Course Textbook
There are many good textbook on machine learning, some which are listed below. We will not follow a single textbook but I will provide lecture notes for most of the material.
Example textbooks for further studies:
Kevin Murphy; Machine Learning: A Probabilistic Perspective; MIT Press
Ethem Alpaydim; Introduction to Machine Learning; MIT Press
Trevor Hastie, Robert Tibshirani, and Jerome Friedman; The Elements of Statistical Learning: Data Mining, Inference, and Prediction; Springer
Assignments
Assignments are posted in the schedule below. Late assignments are not excepted.
Grading Scheme
Assignments 50%, Midterm 20%, Final 30%. Some of the assignment are different between the ugrad and grad version. Some of the assignments may be group work, but you have to pass all individual components in order to pass the course.
Schedule
This schedule is tentative and will likely change frequently. Please check frequently as it provides links to relevant resources and assignments.
| Date | Content | Reference | Assignment |
|---|---|---|---|
| Sept 10 | Intro | ||
| Sept 15 | Overview | http://www.cs.ubc.ca/~murphyk/MLbook/pml-intro-22may12.pdf | |
| Sept 17 | Matlab | http://www.mathworks.com/academia/student_center/tutorials/launchpad.html | A1 |
| Sept 22 | Supervised learning project 1: SVM | http://www.mathworks.com/help/stats/support-vector-machines-svm.html#bsr5b6n <br\> http://www.mathworks.com/help/stats/support-vector-machines-svm.html#bss0s6_-1 | A2 data1 |
| Sept 24 | Basic math (matrices & gradients) | math.pdf | |
| Sept 29 | Supervised learning project 2: MLP | MLP.pdf | A3 |
| Oct 1 | MLP _(... continue) | example program (online, component-wise) mlpANDonlineComponent.m | |
| Oct 6 | MLP _(... continue) | example program (batch, matrix): mlpXORbatch.m | |
| Oct 8 | Classification 1 (SVM) | SVM1.pdf | |
| Oct 13 | Deep Learning 1 | http://www.robots.ox.ac.uk/~vgg/practicals/cnn/index.html | A4 |
| Oct 15 | Deep Learning 2 | Quiz 2 (MLP/SVM) | |
| Oct 20 | Deep learning 3 | ||
| Oct 22 | Deep learning 4 | ||
| Oct 27 | Statistics | Prob1.pdf | |
| Oct 29 | Stochastic modeling (regression and Maximum Likelihood) | Regression1.pdf | |
| Nov 3 | Graphical models | graphical1.pdf https://code.google.com/p/bnt/ | A5 |
| Nov 5 | MDP | MDPslides.pdf | Quiz 3 (Deep learning, CNN, Stochastic modeling) |
| Nov 10 | POMDP/ TD(lambda) | RL.pdf | A6: Exercise 1-4 (+5 for 6505) from RL.pdf (Due Nov 20 by email with subject line A6) |
| Nov 12 | Study Day | ||
| Nov 17 | Generative models, Discriminant Analysis, Naive Bayes | Classification2.pdf | |
| Nov 19 | Unsupervised Learning (k-means, EM algorithm) | Unsupervised1.pdf ExpectationMaximization.m | Quiz 4 (RL) |
| Nov 24 | Unsupervised Learning (Dimensionality reduction, t-SNE, representational learning, DAE, RBM) | Unsupervised2.pdf DimensionalityReduction.pdf RBMExample.zip | 20newsgroups.zip A7 |
| Nov 26 | |||
| Dec 1 | Summary | ||
| Dec 3 | Quiz 5 |
Academic Integrity & Plagiarism
(Based on the sample statement provided at http://academicintegrity.dal.ca. Written by Dr. Alex Brodsky.)
Please familiarize yourself with the university policy on Intellectual Honesty. Every suspected case will be reported.
At Dalhousie University, we respect the values of academic integrity: honesty, trust, fairness, responsibility and respect. As a student, adherence to the values of academic integrity and related policies is a requirement of being part of the academic community at Dalhousie University.
What does academic integrity mean?
Academic integrity means being honest in the fulfillment of your academic responsibilities thus establishing mutual trust. Fairness is essential to the interactions of the academic community and is achieved through respect for the opinions and ideas of others. Violations of intellectual honesty are offensive to the entire academic community, not just to the individual faculty member and students in whose class an offence occurs. (see Intellectual Honesty section of University Calendar)
How can you achieve academic integrity?
• Make sure you understand Dalhousies policies on academic integrity.
• Give appropriate credit to the sources used in your assignment such as written or oral work, com- puter codes/programs, artistic or architectural works, scientific projects, performances, web page designs, graphical representations, diagrams, videos, and images. Use RefWorks to keep track of your research and edit and format bibliographies in the citation style required by the instructor (http://www.library.dal.ca/How/RefWorks)
• Do not download the work of another from the Internet and submit it as your own.
• Do not submit work that has been completed through collaboration or previously submitted for another assignment without permission from your instructor. • Do not write an examination or test for someone else.
• Do not falsify data or lab results.
These examples should be considered only as a guide and not an exhaustive list.
What will happen if an allegation of an academic offence is made against you?
I am required to report a suspected offence. The full process is outlined in the Discipline flow chart, which can be found at: http://academicintegrity.dal.ca/Files/AcademicDisciplineProcess.pdf and in- cludes the following:
1. Each Faculty has an Academic Integrity Officer (AIO) who receives allegations from instructors.
2. The AIO decides whether to proceed with the allegation and you will be notified of the process.
3. If the case proceeds, you will receive an INC (incomplete) grade until the matter is resolved.
4. If you are found guilty of an academic offence, a penalty will be assigned ranging from a warning to a suspension or expulsion from the University and can include a notation on your transcript, failure of the assignment or failure of the course. All penalties are academic in nature.
Where can you turn for help?
• If you are ever unsure about ANYTHING, contact myself.
• The Academic Integrity website (http://academicintegrity.dal.ca) has links to policies, defini tions, online tutorials, tips on citing and paraphrasing.
• The Writing Center provides assistance with proofreading, writing styles, citations.
• Dalhousie Libraries have workshops, online tutorials, citation guides, Assignment Calculator, Ref- Works, etc.
• The Dalhousie Student Advocacy Service assists students with academic appeals and student discipline procedures.
• The Senate Office provides links to a list of Academic Integrity Officers, discipline flow chart, and Senate Discipline Committee.
Request for special accommodation
Students may request accommodation as a result of barriers related to disability, religious obligation, or any characteristic under the Nova Scotia Human Rights Act. Students who require academic accommodation for either classroom participation or the writing of tests and exams should make their request to the Advising and Access Services Center (AASC) prior to or at the outset of the regular academic year. Please visit www.dal.ca/access for more information and to obtain the Request for Accommodation – Form A.
A note taker may be required as part of a student’s accommodation. There is an honorarium of $75/course/term (with some exceptions). If you are interested, please contact AASC at 494-2836 for more information.
Please note that your classroom may contain specialized accessible furniture and equipment. It is important that these items remain in the classroom, untouched, so that students who require their usage will be able to participate in the class.
