This syllabus is subject to change based on specific class needs, especially the schedule. Significant deviations will be discussed in class. Individual exceptions to the policies and schedule are granted only in cases of true emergency. Please make arrangements with me if an emergency arises.
An introduction to machine learning with topics in data science and data mining. The course aims to supply students with a useful toolbox of machine learning techniques that can be applied to real-life data. Techniques may include logistic and linear regression, SVMs, decision trees, neural networks, and clustering. The focus will be on developing important skills in preparing data and selecting and evaluating models, though we will delve into the mathematical intuition behind each model.
Possible topics include:
The required course textbook is:
Géron, Aurélien. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems (2nd edition). O’Reilly. 2019. ISBN-13: 978-1492032649
I also recommend, but do not require:
Guido, Sarah and Muller, Andreas. Introduction to Machine Learning with Python. O’Reilly. 2016. ISBN-13: 978-1449369415.
We might also use some material from:
Deisenroth, Marc Peter and Faisal, A. Aldo and Ong, Cheng Soon. Mathematics for Machine Learning. Cambridge University Press. 2020. ISBN-13: 978-1108455145. Available at https://mml-book.github.io/.
We’ll be using Python 3 along with several of the standard data science libraries available, including matplotlib, pandas, numpy, and scikit-learn, among others. The easiest way to get set up is to install the Anaconda Python distribution, which includes everything you need. It also includes the Spyder IDE for development, although you are free to develop in any text editor you like. You may also use the department server if you’d like; just send me an email.
Late assignments: You have each been allotted a total of 5 late days. You may apply these to any homework or programming assignment (NOT exams, labs, or reading assignments) you see fit and turn in your solutions with no penalty. Each late days gives you exactly 24 extra hours from the original due date and time. However, you may use at most 2 late days on any individual assignment. The whole point here is to give you some flexibility that allows for things like illnesses, long trips, and the like. I am unlikely to grant further extensions. You must notify me if you will use late days, and how many, BEFORE the assignment is due. Late assignments (beyond any applied late days) will be subject to a grade reduction at my discretion. While you should expect a reasonable penalty for late work, know that I will NEVER give you a 0 for late work as long as it is turned in before the final exam.
Academic dishonesty: Monmouth College’s official policy on academic dishonesty can be found here. You are responsible for reading and complying with that policy.
In this course, any violation of the academic honesty policy will have varying consequences depending on the severity of the infraction as judged by the instructor. Minimally, a violation will result in an “F” or 0 points on the assignment in question. Additionally, the student’s course grade may be lowered by one letter grade. In severe cases, the student will be assigned a course grade of “F” and dismissed from the class. All cases of academic dishonesty must be reported to the Associate Dean who may decide to recommend further action to the Admissions and Academic Status Committee, including suspension or dismissal. It is assumed that students will educate themselves regarding what is considered to be academic dishonesty, so excuses or claims of ignorance will not mitigate the consequences of any violations
Collaboration: We encourage you to make use of the resources available to you – it is fine to seek help from a friend, tutor, instructor, internet, etc. However, copying of answers and any act worth of the label “cheating” is never permissible! In addition to listing your sources and collaborators, you should be producing your own writeup in your own words. By “your own words,” we mean you should be producing the text yourself, without some external aid. Verbatim copying of text is specifically disallowed, but so is taking a source and rearranging some phrases and changing some variable names to create a derivative version! Such behavior is definitely NOT “using your own words.” It does not matter if you helped contribute to this source text with others, since then you are still not the sole author of the text. The point of collaborating on an assignment is not to produce a jointly authored set of solutions, since that violates the course policies. Instead, it is to help you solve the problems, which sometimes involve a bit of creativity. After you have jointly come up with the ideas you need to solve the problems, though, you should part ways with your group and sit down to do the writing by yourself. I also advise against sharing the writeup you submit with others, since if someone else uses your text as a source for their own solution (with or without your permission), you will also be implicated in the violation of the academic integrity policy. In any case, if two nearly identical solutions are received, we have no way of tell which is the original, and the policy is to not award credit for either submission.
Electronic devices: Do not use your phone or other devices in class except where necessary. Any computer or tablet usage should be related to the course. If a device is not being used for Zoom or Socrative it should be put away and turned on silent. Other usage is rude and distracting to others.
General expectations: In short, I expect you to be respectful of others and take responsibility for your own learning. You are here to learn, so work hard and be professional.
Just attending class is not sufficient to truly learn the material. Read the text, use the resources available at Monmouth College, and go beyond the material.
If you miss class, you are responsible for everything covered on that day. College is, in some sense, your job. Take pride in creating quality work. Staple your assignments, label problems, and present your answers neatly and orderly.
Your job is to convince me that you have learned the material – show your work! Even if you do not know a particular answer, guide me through your thought process.
The course workload is as follows:
| Category | Number of Assignments | Final Grade Weight |
|---|---|---|
| Homework | 5–7 | 50% |
| Midterm | 1 | 20% |
| Final | 1 | 20% |
| Participation | - | 10% |
Most (probably all) homework assignments will involve programming. Each exam focuses primarily, but not necessarily exclusively, on material covered since the previous exam. In other words, the final exam may include one or two questions from first-half material.
Your participation grade is based on a variety of activities. During class I will often make sure of the Socrative app, so you’ll need to install this on your phones. Participating in Socrative questions and with in-class group activities is required for a decent participation grade; a full grade also includes asking questions either in class or in office hours.
Your final grade is based on a weighted average of particular assignment categories, with weights shown above. You can estimate your current grade based on your scores and these weights. You may always visit the instructor outside of class to discuss your current standing.
This courses uses a standard grading scale. Assignments and final grades will not be curved except in rare cases when its deemed necessary by the instructor. Percentage grades translate to letter grades as follows:
| Score | Grade |
|---|---|
| 94–100 | A |
| 90–93 | A- |
| 88–89 | B+ |
| 82–87 | B |
| 80–81 | B- |
| 78–79 | C+ |
| 72–77 | C |
| 70–71 | C- |
| 68–69 | D+ |
| 62–67 | D |
| 60–61 | D- |
| 0–59 | F |
You are always welcome to challenge a grade that you feel is unfair or calculated incorrectly. Mistakes made in your favor will never be corrected to lower your grade. Mistakes made not in your favor will be corrected. Basically, after the initial grading your score can only go up as the result of a challenge*.
The weekly workload for this course will vary by student and over the semester, but on average should be about 12 hours per week. The follow table provides a rough estimate of the distribution of this time over different course components for a 16 week semester.
| Category | Total Time | Time/Week (Hours) |
|---|---|---|
| Lectures | 55 | 2.5 |
| Homework | 72 | 4.5 |
| Exam Study | 27 | 1.5 |
| Reading+Unstructured Study | 2.5 | |
| 11 |
The following tentative calendar should give you a feel for how work is distributed throughout the semester. Assignments and events are listed in the week they are due or when they occur. This calendar is subject to change based on the circumstances of the course.
| Date | Topic | Assignment/Reading |
|---|---|---|
| Wed 08/24 (Week 1) | Intro and Logistics | |
| Fri 08/26 | ML Landscape | Ch. 1 (p. 1–32) |
| Mon 08/29 (Week 2) | Python Libs, NumPy | NumPy Tutorial, Hwk 1 |
| Wed 08/31 | Pandas | Pandas Tutorials (all except time series) |
| Fri 09/02 | Visualization (matplotlib) | Matplotlib Basic Usage |
| Mon 09/05 (Week 3) | Regression Case Study (notebook) | p. 35–61 |
| Wed 09/07 | Model Selection & Validation | p. 62–83 |
| Fri 09/09 | Preprocessing I | Hwk 2 |
| Mon 09/12 (Week 4) | Preprocessing II | p. 85–100 |
| Wed 09/14 | Imputation; Binary Classification Evaluation | p. 100–109 |
| Fri 09/16 | Multiclass Classification | |
| Mon 09/19 (Week 5) | Calibration & Imbalanced Data | |
| Wed 09/21 | Calculus Review | 3blue1brown (Ch. 1-5) |
| Fri 09/23 | Basic Linear Algebra | 3blue1brown (All but ch. 12), Hwk 3 |
| Mon 09/26 (Week 6) | Training Models | p. 111–128, Hwk 3 Leaderboard |
| Wed 09/28 | Feature Engineering (notebook) | |
| Fri 09/30 | Complexity & Regularization | p. 128–142 |
| Mon 10/03 (Week 7) | (Class Cancelled) | p. 142–151 |
| Wed 10/05 | Logistic Regression and SVMs | p. 153–164 |
| Fri 10/07 | SVM Kernels | p. 164–174 |
| Mon 10/10 (Week 8) | Midterm | |
| (Wed 10/12) | (Fall Break) | |
| (Fri 10/14) | (Fall Break) | |
| Mon 10/17 (Week 9) | Midterm Solutions | |
| Wed 10/19 | Decision Trees | p. 175–187 |
| Fri 10/21 | Ensembles | p. 189–211 |
| Mon 10/24 (Week 10) | Hwk3 Questions | |
| Wed 10/26 | (No class – work on Hwk3) | |
| Fri 10/28 | Model Interpretation & Feature Selection | |
| Mon 10/31 (Week 11) | Dimensionality Reduction | Ch. 8 |
| Wed 11/02 | Clustering | |
| Fri 11/04 | Mixture Models | |
| Mon 11/07 (Week 12) | (No class) | |
| Wed 11/09 | NMF and Outlier Detection | |
| Fri 11/11 | Working with Text | |
| Mon 11/14 (Week 13) | More Text: Topic Models | Hwk 4 |
| Wed 11/16 | LDA | Final Project |
| Fri 11/18 | Word and Document Embeddings | |
| Mon 11/21 (Week 14) | Hwk 3 Review | |
| (Wed 11/23) | (Thanksgiving Break) | |
| (Fri 11/25) | (Thanksgiving Break) | |
| Mon 11/28 (Week 15) | Neural Networks | Ch. 10 |
| Wed 11/30 | ||
| Fri 12/02 | Training Neural Networks | Ch. 11, Hwk 5 |
| Mon 12/05 (Week 16) | Transfer Learning; RNNs | |
| Wed 12/07 | Transformers | |
| Sat 12/10 3:00 PM | Final Exam |
Mental Health and Counseling Services: Monmouth College provides cost-free, professional mental health counseling to support you and to help you manage challenges that may impact your personal and academic success. The Counseling Center is located in the uppler level of Poling Hall (rooms 204 and 216), and the hours are Monday–Friday, 8:30 AM to 5:00 PM. For an appointment call 309-457-2115, email counselingservices@monmouthcollege.edu, cbeadles@monmouthcollege.edu, or tcaudill@monmouthcollege.edu, or request an appointment directly by going to titanium.monmouthcollege.edu and clicking on “request an appointment.”
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