Instructor: Todd Dole
Semester: Spring 2026
Meeting Times: MWF 10-10:50 AM
First Day of Class:January 12
Location: AH 204
Holidays / Breaks:January 19 (MLK Day), March 9-13 (Spring Break), April 3 (Easter)
Contact: todd.dole@hsutx.edu, Phone 325-670-1502, Office AH100
Monday: 2:15-4:15
Tuesday: 9:00-12:00
Wednesday: 2:00-4:00
Thursday: 9:00-12:00
Other times by appointment.
This course introduces students to the field of Data Science, and Machine Learning techniques (includuing supervised and unsupervised learning.)
Required Textbook: Why Machines Learn by Anil Ananthaswamy, and Hands-On Machine Learning by Aurelien Geron, available on Cowboy Access
Weekly readings will be assigned from these textbook. We will cover some of the book material in lectures, but not all. You will be responsible to know all information from the assigned chapters.
| Week | Date | Topic | Reading | Assignment |
|---|---|---|---|---|
| 1 | January 12, 14, 16 | Introduction to the Course. Introduction to Data Mining Pipeline. Software Setup, Jupyter Notebooks |
Skim WML Chapter 1. Read HOML Chapter 1. | Set up Accounts on Kaggle, Google Colab |
| 2 | January 21, 23 | Data Attributes, Data Visualization | Skim WLM Chapter 2. Read HOML Chapter 2 pp. 39-62. Do not be overwhelmed at the details--this is an overview chapter and we will cover each topic much more thoroughly. | No Class Monday MLK Day. Reading Quiz Wednesday |
| 3 | January 26, 28, 30 | Data Sources. Exploratory Data Analysis Part 1: Data Cleaning, Normalization | Skim WML Chapter 3. Read HOML chapter 2 pp. 63-75 | EDA Lab |
| 4 | February 2, 4, 6 | EDA Part 2: Correlation Analysis, Dimensionality Reduction, Data Preprocessing | Read HOML chapter 2 pp 75-101. | Exam 1 Friday |
| 5 | February 9, 11, 13 | Introduction to Machine Learning. Binary Classifier and Multiclass Classifier using MNIST. | Read WML chapter 4, HOML chapter 3 | Jupyter Lab Framework Assignment. Reading Quiz Friday |
| 6 | February 16, 18, 20 | Supervised Learning: Linear, Polynomial Regression | Read HOML chapter 4 | |
| 7 | February 23, 25, 27 | Supervised Learning Model Assessment | Read WML chapter 5. No class Friday (Mr. Dole @ Texas Academy of Science Conference) | Intro to Kaggle Challenge #1. |
| 8 | March 2, 4, 6 | Support Vector Machines | Read HOML chapter 5. | Intro to Supervised Learning Project. Reading Quiz Friday |
| 9 | March 16, 18, 20 | Curse of Dimensionality, Principal Component Analysis (PCA) | Read WML chapter 6, HOML chapter 8 | Kaggle Challenge 1 due. |
| 10 | March 23, 25, 27 | Decision Trees, Random Forest | Read HOML chapter 6 | |
| 11 | March 30, April 1 | Random Forest Part 2 | Read HOML chapter 7. | Exam 2 Wednesday. No class Friday (happy Easter!) |
| 12 | April 6, 8, 10 | Clustering, KNN, DBSCAN | Read HOML chapter 9 | Intro to Kaggle Challenge 2. Supervised learning project due. |
| 13 | April 13, 15, 17 | Recommender Systems, Similarity | Intro to Unsupervised Learning Project | |
| 14 | April 20, 22, 24 | Matrix Factorization | ||
| 15 | April 27, 29, May 1 | Anomaly Detection, Final Exam Review | Unsupervised Learning Project Due | |
| 16 | May 4-7 | Final Exams |
Your grade in the course will be earned / calculated as follows:
A: 90-100
B: 80-89
C: 70-79
D: 60-69
F: 0-59
The final exam will take place at the scheduled time during finals week. Exams will never be collaborative in nature, so receiving any form of assistance from anyone other than the instructor is a violation of the academic integrity policy. You may only use study aids during the exam if they are expressly allowed by the instructor for that particular exam.
An individual with a disability is defined by the Americans with Disabilities Act (ADA) as a “person who has a physical or maaental impairment that substantially limits one or more major life activities.” Any student with a documented disability may choose to seek accommodations. Eligible students seeking accommodation should contact the Director of Undergraduate Advising and Disabilities as soon as possible in the academic term (preferably during the first two weeks of a long semester) for which they are seeking accommodations. The director will prepare letters to appropriate faculty members concerning specific, reasonable academic adjustments for the student. The student is responsible for delivering accommodation letters and conferring with faculty members. Please refer to the most recent version of the Undergraduate Catalog for the complete policy. (Carol Krueger, Director of Undergraduate Advising and Disabilities, Office: Sandefer Memorial, 1st floor Academic Advising Center, Phone: 670-5867, Email: disabilityservices@hsutx.edu)
Peer-to-peer academic support (tutoring) is available for all undergraduate HSU students. The Academic Center for Enrichment (ACE) is open for virtual tutoring sessions via Zoom. To access instructions or make an appointment, open the ACE course on your Canvas dashboard. For additional information regarding academic support, contact the Advising Center at 325-670-1480 or tutoring@hsutx.edu.
In addition, all full or part-time students are eligible to receive free, confidential, and voluntary counseling services at HSU. Services include consultation, evaluation, counseling, and crisis support services for students facing issues impacting their overall well-being. To obtain any of these services, students may call The Office of Counseling Services at (325) 671-2272, email counseling@hsutx.edu, or begin the intake process by completing our online forms at https://www.hsutx.edu/intake.
Violations of academic integrity have been described to some degree in other sections of this syllabus. Cases of suspected academic dishonesty will be handled in accordance with university policies outlined in the Undergraduate Catalog and in the Student Handbook. The current catalog prescribes that “no student who has violated the Academic Integrity Policy will be allowed to graduate from Hardin-Simmons University with honors.” Penalties will be assigned at the discretion of the instructor and typically range from failure on the assignment to failure of the course. A general rule-of-thumb is that a first offense (if not too major) will result in a zero on the assignment and a second offense will result in an F for the course. The current catalog states that an F earned in this way cannot be replaced by retaking the course.
See AI Statement in Simple Syllabus.
The instructor may occasionally use email to communicate with the class as a whole or with individuals. When contacting you for this course the instructor will use your HSU email account. You are expected to check your HSU email account at least once per day and you will be held responsible for any content distributed in this way.
Regarding class attendance, the Undergraduate Catalog states:
Accordingly, absence from more than 25 percent of class meetings and/or laboratory sessions scheduled for a course (including absences because of athletic participation) is regarded as excessive, and a grade of F may be assigned as deemed appropriate by the professor.