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Syllabus

Table of contents

  1. Syllabus
    1. Course Description
    2. Textbook
    3. Office hours
    4. Learning Goals
    5. Grading
    6. Additional Course Policies
      1. Class Attendance and Participation
      2. Late Policy and Make-ups
      3. Extensions
      4. Academic Honesty
      5. Course Withdrawal Options
    7. Inclusive Learning Statement
    8. Title IX Reporting Policy Regarding Sexual Misconduct, Harassment, Relationship Violence, and Stalking
    9. Wellbeing at Wooster
    10. Discriminatory or Bias-Related Harassment Reporting Policy Contact
    11. The Learning Center: Academic Support and Disabilities

Course Description

This course is a hands-on introduction to machine learning. The main question addressed is: How can we design good computer algorithms that improve automatically through experience (e.g. similar to the way humans learn)? Multiple machine learning models are examined. The goal of the course is that students begin to understand some of the issues and challenges facing machine learning while being exposed to the pragmatics of implementing machine learning systems. We will cover topics such as decision trees, artificial neural nets, deep learning, Bayesian learning, k-means clustering, and genetic algorithms.

Course assumes familiarity with Python programming, linear algebra, basic probability theory.

Course Prerequisites: Math 120, 130, and any CS 2xx

Textbook

Introduction to Machine Learning with Python: A Guide for Data Scientists. Andreas Müller and Sarah Guido, O’Reilly Media, ISBN-13: 978-1449369415. You may access this for free via our libraries: Wooster Libraries -> Databases->O’Reilly Safari Learning Platform

Machine Learning. Tom Mitchell, McGraw-Hill, ISBN: 978-0-07-042807-2.

Additional resource:
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, by Aurélien Géron, O’Reilly Media, 3rd Edition.

Machine Learning with Python Cookbook, by Kyle Gallatin, Chris Albon, O’Reilly Media, 2nd Edition.

Software: we will be using Python programming and the JetBrain DataSpell IDE. This IDE allows us to both test solutions in Jupyter Notebook and to create stand-alone Python programs - please see Lab1 for software installation. For your final project you are allowed to use libraries such aso scikit-learn; TensorFlow from Google; Keras; PyTorch by Facebook; Google Colab.

Office hours

MW 10-11, W 2-3 in Taylor 304; E-mail: svisa@wooster.edu

Learning Goals

  • Explain a wide variety of learning algorithms such as decisin trees, neural networks, k-nearest neighbors, k-means clustering, Bayes classifier.
  • Apply the algorithms to real-world problems and report on the expected accuracy
  • Explain results and models generated from data
  • Compare and contrast different paradigms for learning (supervised, unsupervised, etc.)

Grading

  • 20% Assignments and quizzes
  • 20% Labs and class activities
  • 30% Two exams, 15% each
  • 30% Final project and presentation

This class uses the following standard grading scale:

  B+: 87-90 C+: 77-80 D: 60-70
A : 93-100 B : 83-87 C : 73-77 F: below 60
A-: 90-93 B-: 80-83 C- 70-73  

The main purpose of assignments, labs, and activities is to give students opportunities to practice what they learn. I hope students understand the value of this work, and do not regard it solely as a grading source. With this in mind, the instructor will choose to grade only a subset of this work. The remaining subset could be graded partially, or based on whether the work was submitted.

Additional Course Policies

Class Attendance and Participation

Class attendance is mandatory. Please come to class prepared, with the assigned reading done. It is College of Wooster policy that a student may not miss more than 25% of class meetings (e.g., ~3.5 weeks of class for a full-credit course in fall/spring semesters), through any combination of excused and unexcused absences. If this occurs, the instructor will notify the student and the Dean for Curriculum and Academic Engagement for consultation. For health reasons and other exceptional circumstances, you may miss up to four classes; each absence (for any reason) beyond the allowable number will result in a 5% reduction in your final course grade.

Late Policy and Make-ups

Late submissions make timely grading much more difficult. As such, any submissions I receive after the class of the due date will be subject to a 25% penalty for each day that it is late.

Make-ups will be given for a test only in special situations; let me know in advance if you have to miss a test. No other make-ups will be given.

Extensions

I will extend an assignment’s due date for the entire class if it is clear that the original time frame was unreasonable. If you are going to bring up the possibility of a due date extension for a programming project, be prepared to demonstrate that you have already made substantial progress on the project.

I will grant personal extensions under the right circumstances. If you would like an extension for personal reasons, send me an email or come see me during office hours.

Academic Honesty

You are encouraged to discuss homework assignments and programming projects with other students. However, any uncited work you turn in must be your own.

Software similarity is a tricky thing as some similarities between code submissions are inevitable. I draw the line at submissions that contain a significant amount of code that is either identical to someone else’s code or submissions where the only differences are purely cosmetic (i.e. variable and function names have been changed to hide code copying). If in doubt, put a comment in your code citing a source or acknowledging collaboration. Contact me if you have any doubts about what is permissible.

Dishonesty in any of your academic work is a serious breach of the Code of Academic Integrity and is grounds for an F for the entire course. Such violations include turning in another person’s work as your own, copying from any source without proper citation, crossing the boundary of what is allowed in a group project, submitting an assignment produced for a course to a second course without the authorization of all the instructors, and lying in connection with your academic work. You will be held responsible for your actions.

You are expected to know and abide by the rules and policies of the institution as described in the documents available here

Course Withdrawal Options

Students may withdraw from a course after the 6th-week drop deadline until the last day of classes (Friday, December 9, 2022 in Fall 2022 and Tuesday, May 9, 2023 in Spring 2023). Students may withdraw from one course, up to 1.25 credits, at any time through the last day of classes, as long as their total remaining credits are 3.0 or above. This may be done without documentation of extenuating circumstances. Requests to drop enrollment below 3.0 credits will require additional documentation through a Petition for an Exception to an Academic Policy. Note that because federal government guidelines define courses as ‘attempted’ after 6 weeks, if a student withdraws from a course after 6 weeks, it will be noted as a ‘W’ on their transcript

Inclusive Learning Statement

Your success in this course is important to me. If there are circumstances that may affect your academic performance or impact your learning in particular portions of the class, please let me know as soon as possible. You do not need to share specifics, but together we can develop strategies to meet both your needs and the requirements of the course. I encourage you to visit the Academic Resource Center to determine how you could improve your learning as well. If you need official accommodations, the ARC can work with you to make sure your needs are met. There are also a range of resources on campus, including the Writing Center, Math Center, STEM Success Initiative, and APEX.

It is also important that we all be respectful of everyone’s privacy around health concerns, vaccination status, and any accommodations that are necessary in the classroom. It is not appropriate to question why someone requests physical distancing, chooses to wear a mask, or requires any other accommodations. As part of our participation together in this class, we commit to showing respect to each other as individuals, to working together to create a learning environment that fosters a sense of belonging and inclusion to all members, and to understanding that our differences are also strengths. Your suggestions are encouraged and appreciated, and please contact me—via email, office hours, or after class—if you have any concerns or questions.

Title IX Reporting Policy Regarding Sexual Misconduct, Harassment, Relationship Violence, and Stalking

Contact: Joe Hall, jhall@wooster.edu

The College of Wooster is committed to fostering a campus community based on respect and nonviolence. To this end, we recognize that all Wooster community members are responsible for ensuring that our community is free from discrimination, gender bias, sexual harassment, and sexual assault. In accordance with Title IX, Wooster is legally obligated to provide supportive options for all reports of sexual harassment and sexual assault that occur on our campus. Faculty who become aware of an incident of sexual violence, including harassment, rape, sexual assault, relationship violence, or stalking, are mandated reporters at the College and are required to notify Wooster’s Title IX Coordinator. The purpose of this disclosure is to ensure that students are made aware of their reporting options and resources for support. For more information about your rights and reporting options at Wooster, including confidential and anonymous reporting options, please visit http://www.wooster.edu/offices/title-ix/.

Wellbeing at Wooster

Contact (24/7): (330) 263-2319, or visit the Wellness Center website

The College of Wooster is committed to supporting the wellbeing of our students. During the course of their academic careers, students experience challenges that may interfere with their learning & health (both physical and mental), including but not limited to: strained relationships, adjusting to a new environment, chronic worrying, persistent sadness or loss of interest in enjoyable activities, family conflict, grief and loss, domestic violence, unwanted sexual experiences, difficulty concentrating, drug/alcohol problems, significant changes in eating and sleeping patterns, microaggressions, challenges with organization, procrastination and/or lack of motivation. Counseling Services at the Longbrake Student Wellness Center is a free and confidential resource providing short-term counseling and connections to community agencies for students needing longer term or specialized resources. You can make an appointment by calling 330.263.2319 between 8:30am-4:30pm during weekdays or by emailing Lori Stine (lstine@wooster.edu). You can also find helpful resources on the Counseling Services website at https://inside.wooster.edu/health/counseling/.

Students also have free access to TimelyCare, a telehealth service providing scheduled medical and counseling appointments as well as 24/7 crisis consultation with licensed professionals. Students use their Wooster email to establish an account at TimelyCare: Telehealth for Scots. TimelyCare also provides students access to nutritionists and health coaches about issues of sleep and exercise, and psychiatry (with a referral from doctor or counselor).

If you or a friend is in crisis, please call Campus Safety at 330-287-3333 or the Suicide and Crisis Lifeline (988, available 24 hours) or connect with the Crisis Text Line by Texting “4HOPE” to 741-741. For financial concerns: Dean of Students Office, dos@wooster.edu (330) 263-2545, DoS website For safety concerns: Campus Safety 330-263-2590 or cow-security@wooster.edu, Campus Safety website. In the care of an emergency, call: 330-287-3333.

The College of Wooster is committed to promoting its mission of inclusivity and equity in all aspects of the educational enterprise. This commitment extends to all rights, privileges, programs and activities, including housing, employment, admissions, financial assistance, and educational and athletic programs at the College. The College’s Bias Incident Reporting Process is designed to effectively respond to bias concerns raised by faculty, students, staff, alumni and visitors to the College. If you or someone you know are the victims of bias, you can: File a report online (where you may choose to identify yourself or not)

  • Contact Campus Safety: 2590 (from campus phone) or 330-263-2590

  • Call the Anonymous Tip Line: 2337 (from campus phone) or 330-263-2337

  • Contact the Dean of Students Office: 2545 (from a campus phone) or 330-263-2545

  • Contact the Vice President for Equity, Inclusion, and Diversity Cheryl Nuñez at 330-263-2356

The Learning Center: Academic Support and Disabilities

The Learning Center, which is in APEX (Gault library) offers a variety of academic support ser- vices, programs and 1:1 meetings available to all students. Popular areas of support include time management techniques, class preparation tips and test taking strategies. In addition, the Learn- ing Center coordinates peer-tutoring for several academic departments. Students are encouraged to schedule an appointment at the APEX front desk or visit the Learning Center Website for additional options.

An additional support that the Learning Center offers is English Language Learning. Students can receive instruction or support with English grammar, sentence structure, writing, reading compre- hension, reading speed, vocabulary, listening comprehension, speaking fluency, pronunciation, and American culture through 1:1 meetings with the Learning Center staff, ELL Peer Tutoring, ELL Writing Studio courses, and other programming offered throughout the year. Students seeking ELL support are encouraged to visit the APEX front desk.

The Learning Center also coordinates accommodations for students with diagnosed disabilities. At the beginning of the semester, students should contact the Learning Center (ext. 2595) to make arrangements for securing appropriate accommodations. Although the Learning Center will notify professors of students with documented disabilities and the approved accommodations, students are encouraged to speak with professors during the first week of each semester. If a student does not request accommodations or does not provide documentation to the Learning Center, faculty are under no obligation to provide accommodations.

The details of this syllabus are subject to change based on our progress through the material.