From Teaching

ML: Syllabus

CS 429/529

Course description:

Introduction to principles and practice of systems that improve performance through experience. Topics include statistical learning framework, supervised and unsupervised learning, performance evaluation and empirical methodology; design tradeoffs.

Textbook

Most lectures will be based on the following optional textbooks:


POLICIES

Academic honesty:

Unless otherwise specified, you must write/code your own homework assignments. You cannot use the web to find answers to any assignment. If you do not have time to complete an assignment, it is better to submit your partial solutions than to get answers from someone else. Cheating students will be prosecuted according to University guidelines. Students should get acquainted with their rights and responsibilities as explained in the Student Code of Conduct

Any and all acts of plagiarism will result in an immediate dismissal from the course and an official report to the dean of students.

Instances of plagiarism include, but are not limited to: downloading code and snippets from the Internet without explicit permission from the instructor and/or without proper acknowledgment, citation, or license use; using code from a classmate or any other past or present student; quoting text directly or slightly paraphrasing from a source without proper reference; any other act of copying material and trying to make it look like it is yours.

Note that dismissal from the class means that the student will be dropped with an F from the course.

The best way of avoiding plagiarism is to start your assignments early. Whenever you feel like you cannot keep up with the course material, your instructor is happy to find a way to help you. Make an appointment or come to office hours, but DO NOT plagiarize; it is not worth it!

Change of grade to CR/NC after the semester deadline will be granted ONLY under special, documented extenuating circumstances.

Class attendance:

Attendance to class is expected (read mandatory) and note taking encouraged. Important information (about exams, assignments, projects, policies) may be communicated only in the lectures. We may also cover additional material (not available in the book) during the lecture. If you miss a lecture, you should find what material was covered and if any announcement was made.

ASSIGNMENTS

Projects:

Projects and homework reinforce what you learned in class by materializing abstract concepts into practical problems. Every project usually consists of a programming assignment. Tentative schedule is as follows:

You can discuss projects with other classmates but all the code have to be written by you. Any student suspected of plagiarizing code will be prosecuted according to the University guidelines.

Weekly quizzes

Readings in this syllabus are mandatory, you can expect short individual quizzes every week. Those quizzes will be graded and they account for 10% of your final grade

Daily assignments

You can expect to have simple exercises every meeting. These daily assignments will be done in groups specified by the instructor and they will account for your participation grade (10% of your final grade)

EXAM

Exams are our formal evaluation tool. In the exams you will be tested with respect to the learning goals of this course (see the schedule below for the list of learning goals). Exams will comprise a mix of practical exercises and concepts. I don't encourage you to learn concepts and definitions by heart, but to be able to explain them with your own words and to place these concepts into the broader context they belong to. There will be one midterm exam around October 19 and one final exam around Nov 16

The exam is open notes, but only personal, hand-written notes are accepted. Restrictions in this matter include (but are not limited): you cannot download notes from Internet, you cannot use the electronic notes of the course, and you cannot photocopy notes from your classmates. †In fact, the key point is that they must be your own hand-written notes because I expect you to reinforce what you learned in class by writing down key concepts.

FEEDBACK

I value student's opinions regarding the course and I will take them in consideration to make this course as exciting and engaging as possible. Thus, through the semester I will ask students formal and informal feedback. Formal feedback includes short surveys on my teaching effectiveness, preferred teaching methods, and pace of the class. Informal feedback will be in the form of polls or in-class questions regarding learning preferences. You can also leave anonymous feedback in the form of a note in my departmental mail box, under my office door (FEC 325), or using this form. Remember that it is in the best interest of the class if you bring up to my attention if something is not working properly (e.g the pace of the class is too slow, the projects are boring, my teaching style is not effective) so that I can make the corrective steps.


GRADING

Grades will be based on your earned points, following this grade scale. You need to get the specified number of points or more to obtain the grade from the same column. Scores will be rounded to the closest integer value.

A	A-	B+	B	B-	C+	C	C-	D+	D	D-	F
97	95	90	85	80	77	73	70	67	63	60	<60

ADA:

In accordance with University Policy 2310 and the Americans with Disabilities Act (ADA), academic accommodations may be made for any student who notifies the instructor of the need for an accommodation. If you have a disability, either permanent or temporary, contact Accessibility Resource Center at 277-3506 for additional information.


SCHEDULE

Introduction

CART

Probability Review

Bayesian vs MLE Approach

Naive Bayes

Linear and Logistic Regression

Bias/Variance Tradeoff, Boosting and Bagging

Bayesian networks: Representation & Inference

Hidden Markov Models

EM algorithm and Gaussian Mixture models

Perceptrons & Neural Networks

Deep Learning

Dimensionality reduction, PCA, SVD

Data representation LSI, ICA, Autoencoders

Introduction to Kernel functions

Support Vector Machines

Instance Based Learning

Unsupervised Learning and Clustering

Learning Theory

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Page last modified on October 06, 2017, at 10:48 AM EST