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 the hot topics of machine learning, 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:
Guido, Sarah and Muller, Andreas. Introduction to Machine Learning with Python. O’Reilly. 2016. ISBN-13: 978-1449369415.
I also recommend, but do not require:
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
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 problem sets you see fit and turn in your solutions with no penalty. However, you may use at most 2 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.
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 |
|---|---|---|
| Wed 08/19 (Week 1) | Intro, Logistics | Python tutorial |
| Fri 08/21 | Python Review | IMLP Ch. 1 |
| Mon 08/24 (Week 2) | More Python, Numpy | Hwk 1 out (Solutions), Numpy tutorial |
| Wed 08/26 | Pandas | Modern Pandas |
| Fri 08/28 | Visualization | |
| Mon 08/31 (Week 3) | Regression, Classification case studies | |
| Wed 09/02 | Model Complexity, kNN, Bias/Variance | Hwk 2 out, IMLP 2.2-2.3 (p. 28 – 46) |
| Fri 09/04 | Cross Validation | IMLP 5.1 (p. 253 – 262) |
| Mon 09/07 (Week 4) | More Cross Validation and Grid Search | IMLP 5.2, 6 |
| Wed 09/09 | Bias in Machine Learning | |
| Fri 09/11 | Linear Models for Regression | IMLP p. 47 – 70 |
| Mon 09/14 (Week 5) | Hwk 2 Discussion, Linear Models for Classification | |
| Wed 09/16 | Linear Models for Classification | |
| Fri 09/18 | Preprocessing | IMLP 3.3 |
| Mon 09/21 (Week 6) | Categorical Features, Missing Values | IMLP 4, Hwk 3 out (Leaderboard) |
| Wed 09/23 | Hwk 2 Discussion | |
| Fri 09/25 | More Preprocessing | |
| Mon 09/28 (Week 7) | Kernel SVMs | IMLP p. 94 – 106 |
| Wed 09/30 | Classification Evaluation | IMLP 5.3 |
| Fri 10/02 | Multiclass Classification | |
| Mon 10/05 (Week 8) | Decision Trees | IMLP p. 72 – 85 |
| Wed 10/07 | Tree Ensembles | IMLP p. 85 – 94 |
| Fri 10/09 | (No class – Pseudo-Fall Break) | |
| Mon 10/12 (Week 9) | Hwk 3 Questions | |
| Wed 10/14 | Midterm Review | |
| Fri 10/16 | Midterm | |
| Mon 10/19 (Week 10) | Calibration; Imbalanced Data | |
| Wed 10/21 | Model Interpretation | |
| Fri 10/23 | Feature Selection | |
| Mon 10/26 (Week 11) | Dimensionality Reduction | IMLP 3.4 |
| Wed 10/28 | Clustering | IMLP 3.1, 3.2, 3.5, Hwk 4 out |
| Fri 10/30 | Text Vectorization and Normalization | IMLP 7 |
| Mon 11/02 (Week 12) | Topic Modeling | |
| Wed 11/04 | Word and document embeddings | |
| Fri 11/06 | Hwk 4 Questions | |
| Mon 11/09 (Week 13) | Neural Networks | IMLP p. 106 – 121, Watch 3blue1brown |
| Wed 11/11 | Neural Networks in Practice | Hwk 5 out |
| Fri 11/13 | Keras; Convolutional Neural Networks | |
| Mon 11/16 (Week 14) | Practical Neural Network Training | |
| Wed 11/18 | Advanced Neural Networks | |
| Fri 11/20 | ||
| Mon 11/23 | Review |
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 upper level of Poling Hall, offices 204 and 216, and the hours are Monday-Friday, 8:30 AM – 5:00 PM. To request a confidential appointment on-line, click here or email the center, Cindy Beadles or Tom Caudill.
Student Success and Accessibility Services offers FREE resources to assist Monmouth College students with their academic success. Programs include supplemental instruction for difficult classes, drop-in and appointment tutoring, and individual academic coaching. The office is here to help students excel academically, since everyone can work toward better grades, practice stronger study skills, and mange their time better.
Accessibility Services: If you have a disability or had academic accommodations in high school or another college, you may be eligible for academic accommodations at Monmouth College under the Americans with Disabilities Act (ADA). Monmouth College is committed to equal educational access. To discuss any of the services offered, please call or visit Student Success and Accessibility Services. SSAS is located in the ACE space on the first floor of Hewes Library, opposite Einstein Bros. Bagels. They can be reached at 309-457-2257 or via email at ssas@monmouthcollege.edu.