| Course Information | Instructor | ||
|---|---|---|---|
| Course | ITD 140 – Machine Learning I | Name | Ryan W. Ammons, JD, MS-Soft.Eng. |
| Format | Online — Synchronous (Zoom) | rammons@nvcc.edu | |
| Section | 70YN (CRN 64205) | Phone | 202.618.9646 (text) |
| Semester | Summer 2026 (2nd 6wk) | Office | AN-CC-120B + Zoom |
| Date/Time | W @ 1900-2230 (7:00-10:30 PM) | Office Hours | W/F @ 1400–1800 |
| Location | Online via Zoom (link in Canvas) | Website | www.ProfAmmons.com |
Course Description and Content Summary
Introduces students to artificial intelligence and machine learning. Examines basic theory, algorithms, and applications. Focuses on feature engineering and machine learning applications within the larger world of artificial intelligence. Part I of II. Lecture 3 hours. Total 3 hours per week.
Course Content Summary: https://www.nvcc.edu/academic/coursecont/summaries/ITD140.pdf
As this is a synchronous online (Zoom) class, students are expected to spend at least FOUR (4) hours a week for independent study outside of assignments/homework.
Student Learning Outcomes
As a result of the learning experience in this course, the student should be able to:
- Machine Learning
- Define and explain the purpose of machine learning
- Define and explain the purpose of artificial neural networks
- Feature Engineering
- Define Feature Engineering, Imputation
- Define and apply mean substitution, back/forward-fill substitution
- Define raw features
- Define and explain how to create derived features, and define: Binarization, Rounding, Binning, Fixed-Width Binning
- Identify and use continuous numerical data
- Define a qualitative variable as it applies to machine learning
- Define a quantitative variable as it applies to machine learning (continuous quantitative variable; discrete quantitative variable)
- Define, create and use categorical data
- Define, explain and apply one-hot encoding
- Define and perform simple feature scaling
- Define and apply min-max scaling (i.e., normalization)
- Define and perform standardization
- Performance Metrics and Tuning
- Define and evaluate basic performance metrics, including accuracy, the confusion matrix, precision, recall, F1 score, and AUC-ROC
- Define and explain bias, variance and the bias-variance trade-off
- Define loss function (define and calculate L1 Norm — least absolute error — and L2 Norm — least squares error)
- Define and explain the significance of hyperparameters
- Supervised Learning
- Define and explain the purpose of supervised learning
- Identify supervised learning algorithms and when they should be used
- Define, explain and perform linear and multivariate regression
- Define classification as a supervised learning prediction task (when to use classification; the k nearest neighbor (knn) and decision tree algorithms; apply classification to a dataset using, e.g., knn and decision trees)
- Neural Networks: define and explain perceptrons; the structure and function of artificial neural networks; implement a neural network classifier and apply it to a sample dataset; the purpose of deep learning
- Unsupervised Learning
- Define and explain the purpose of unsupervised learning
- Identify unsupervised learning algorithms and when they should be used
- Clustering: the purpose of clustering; when clustering is appropriate; apply k-means clustering to a sample dataset
- Describe and apply basic dimensionality reduction
- Responsible AI
- Define fairness
- Define and apply metrics to measure types of bias
- Define approaches to mitigate bias
Course pre- and co-requisite(s): ITE 152 + MTH 154 or higher (prereq); ITD 256 Adv Database Mgmt (coreq)
Technical Competencies
Basic knowledge of Canvas is assumed. Review the following link for more info: https://www.nvcc.edu/canvas/
Although programming experience is not required, a basic understanding of programming concepts is assumed. We will be using Python to explore data as well as train and run machine learning models.
Textbooks
Optional / Excerpts Provided
- Deitel. Intro. to Python for Computer Science and Data Science (Pearson, 978-0-13-540467-6)
- Jamsa. Introduction to Data Mining and Analytics (Jones & Bartlett Learning, 978-1-284-18090-9)
- Bishop. Pattern Recognition and Machine Learning (Microsoft/Cambridge Press, 2006) — freely available as a PDF
Required Materials / Software / Hardware
Students will need access to the following software for the course. Required course software is available on all campus lab computers. Since this is a synchronous online course, you will be required to install and use Respondus LockDown Browser for quizzes and the final exam (instructions are provided in Canvas along with prompts to install).
| Orange | https://www.orangedatamining.com We will use Orange to explore, analyze and visualize data sets—as well as train & run basic ML models. |
|---|---|
| Kaggle | https://www.kaggle.com Browser-based tool for data science and ML. We will use it primarily for its freely available data sets. |
| Anaconda | https://www.anaconda.com/products/individual Individual Edition (open source). A platform that combines many tools/frameworks for data science and machine learning. |
| Other | Other cloud/browser-based tools, such as Google Colab—all available via browser, no installation. |
Course Grading, Examinations & Grade Composition
Grading can comprise of factors such as student participation, discussions, assignments, and exams. Your final grade is based on the following scale:
| PROCTORED ASSIGNMENTS | Percent |
|---|---|
| Quizzes (2–3) | 30% |
| Final Exam | 30% |
| Total Proctored | 60% |
| NON-PROCTORED ASSIGNMENTS | Percent |
|---|---|
| Assignments (~6–8), Project | 40% |
| Total Non-Proctored | 40% |
Schedule
Please note: The instructor reserves the right to adjust the schedule.
| Unit | In-class Activities and Topic(s) | Assignments (details on Canvas) |
|---|---|---|
| 1 | Overview, administrative items Intro to artificial intelligence, machine learning |
Setup cloud accounts (#00) Get familiar with software Assignment #01 |
| 2 | Artificial neural networks – theory and demos Intro to Feature Engineering |
Assignment #02 |
| 3 | Feature Engineering (cont.) Intro to Supervised Learning Quiz 1: AI+ML theory/concepts; Feature engineering |
Assignment #03 |
| 4 | Feature engineering (cont.) Supervised Learning (cont.) |
Assignment #04 |
| 5 | Supervised Learning (cont.) Decision Trees, Classification, Neural Nets, Deep Learning Quiz 2: Feature engineering (cont.); Supervised Learning |
Assignment #05 |
| 6 | Intro to Unsupervised Learning, Clustering Hyperparameter tuning |
Assignment #06 |
| 7 | Responsible AI Quiz 3: Unsupervised Learning; Advanced Topics (e.g., hyperparam. tuning) |
Assignment #07 |
| 8 | Review, Wrap-up Final Exam (comprehensive) |
— |
Current Supplemental Syllabus Inserts
The college and IET Division policies are provided by the college. They can be accessed here:
- 2025 College Syllabus Insert:
View document - 2025 IET Division Syllabus Insert:
View document
Any provisions in my syllabus that conflict with those in either document linked above shall be superseded and replaced by those in the linked documents.
Incomplete Grades
A grade of “I” (Incomplete) is given only when a student has completed the majority of the course (60% or more) and becomes unable to attend class or to complete course requirements near the end of the course due to a mitigating circumstance. Also, read the Withdrawal Policy section below for further grade information.
Mitigating Circumstances
Mitigating circumstances are defined as unavoidable situations that can be verified and documented. Examples would include situations like the serious illness of the student, the serious illness or death of a family member, family financial problems, a change in employment hours, or temporary absence from the area because of employment.
Attendance Policy
Attendance is expected in both synchronous Zoom class meetings and participation in Canvas. Students are expected to attend every scheduled synchronous Zoom class meeting and to enter Canvas multiple times per week. When absence from class becomes necessary, please attempt to inform the instructor ahead of time. Students are responsible for all material missed in class due to an absence. Any instruction missed and not subsequently completed either in class or on Canvas will necessarily affect the grade of the student regardless of the reason for the absence.
Disclaimer
I reserve the right to modify the syllabus contents, policies, and course schedule assignments if I determine that such a change will improve the effectiveness of the course presentation without unfairly penalizing student assessment.
- All assignments must be submitted no later than the instructor assigned due date. Late work will not be accepted or graded beyond the due date and you will receive a grade of zero on the assignment. No partial points will be awarded. Emergency situations will be handled case by case.
- A grade of zero will be calculated into your final grade for any exams or assignments not completed and submitted.
- Exams will be administered through Canvas using Respondus LockDown Browser, with multiple choice, fill-ins, essays, true or false, among other formats. Tests must be completed within a set timeframe and must be taken during the scheduled Zoom class session, on the scheduled date. In the event of a system failure, the backup method for taking a test will be at the discretion of the instructor. If you are caught cheating during an exam, you will receive a zero grade.
- No make-up exams will be given. Emergency situations will be handled case by case.
- Electronic devices must be on silent.
- Plagiarizing is dishonest and a form of cheating. Consequently, plagiarized work will receive an “F,” or a zero. In addition, such a practice may prevent students from passing a course and may result in other disciplinary action. (taken directly from NOVA’s website on plagiarism) If I find that you have plagiarized any work you will receive a zero grade; if it happens again you will be reported to academic affairs for it to appear on your transcript.
NOVA is a place for learning and growing. You should feel safe and comfortable anywhere on this campus. In order to meet this objective, you should: a) let your instructor, his/her supervisor, the Dean of Students or Provost know if any unsafe, unwelcome or uncomfortable situation arises that interferes with the learning process (Campus Police: 703-764-5000); b) inform the instructor within the first two weeks of classes if you have received a special needs or a disability accommodation that may affect your performance in this course.
College and IET Division Policies
Students are responsible for knowing and following the policies in the Student Handbook. The following are highlights of information that students should be aware of as they begin a course.
Academic Integrity Policy
NOVA promotes and emphasizes the importance of honesty in academic work. It is therefore imperative for students to maintain the highest standard of honor in their scholastic work. Academic dishonesty, as outlined in more detail in the Academic Integrity Policy (Policy Number: 224), can include, but is not limited to, cheating on an exam or quiz, submitting work that is not your own (plagiarism), or sharing assessments online. Consequences of academic dishonesty can include a failing grade on an assignment, a failing grade in the course, and may include additional administrative sanctions such as suspension or expulsion from the college. Procedures for disciplinary measures and appeals are outlined in the Academic Integrity Procedures. It is a student’s responsibility to become familiar with the student code of conduct. Lack of awareness is no excuse for noncompliance with NOVA’s policies and procedures.
Accommodations and Accessibility Services
NOVA is committed to ensuring all students have an opportunity to pursue a college education regardless of the presence or absence of a disability. No academically qualified student with a disability will be denied access to or participation in the services, programs, and activities of the College. Your access to and inclusion in this course is important to NOVA and me. Please request your accommodation letter (Memorandum of Accommodations) early in the semester or as soon as you become registered so that we have adequate time to arrange your approved academic accommodations. Returning students must renew their Memorandum of Accommodations (MOA) every semester; these students should submit the request 24 hours or later after enrolling in at least one class. Allow up to 7 business days for the request to be approved.
Accommodations are provided for in-person, online, and remote/synchronous (Zoom) learning. To get started, review NOVA’s Accommodation and Accessibility Services website. Following a meeting with a counselor, you will be issued a Memorandum of Accommodation (MOA). You must provide your MOA to your professors, testing proctor, and/or tutoring center in order to receive your accommodations. You may provide your MOA any time during the semester; however, accommodations are not retroactive. You may email your MOA or provide me with a printed copy. I will send you an email to acknowledge receipt. If I have any questions or if there is anything about your accommodations you wish to explain, we will schedule a meeting outside of class for that purpose. Please remind me of any special arrangements that must be made in advance of tests and assessments. If you need a sign language interpreter, or if you need live captions for your Zoom class, send an email to interpreters@nvcc.edu.
Career Services
The College is committed to providing career services to all students as part of the comprehensive educational journey. Career Services assists students with exploring, developing and setting goals related to each student’s unique educational and academic needs. These services include career assessments, occupational information, goal setting, planning and employment resources. You can request an appointment with a career counselor.
Closing Information
NOVA announces campus and college closings on the NOVA homepage. You can also receive notification by cell phone or email if you register for NOVA Alert. Also review NOVA’s guidance on emergency closings, delayed openings, and continuation of instruction. If a course is canceled due to a weather event or other unforeseen situation, check the course Canvas site or NOVA email as soon as possible for instructions and assignments to avoid falling behind in coursework. You are expected to be up to date with all assignments the next time the class meets.
Communication
Northern Virginia Community College (NVCC) faculty, staff, and administrators communicate with students through their official NVCC email accounts (rammons@nvcc.edu). Students are likewise required to use their VCCS email accounts (@email.vccs.edu) to communicate with instructors and other college personnel. Students should check their email accounts regularly.
Course Drop / Withdrawal Policy
Please note these important deadlines related to your enrollment in a course:
- Students may drop courses through NOVAConnect until the last day to drop with a tuition refund (census date). Students who drop a class during this period will receive a full refund.
- Requests to change your grade status to audit must also be completed before the last day to drop with a tuition refund (census date).
- Students who do not attend at least one class meeting or participate in an online learning class by the last day to drop with a tuition refund (census date) may be administratively deleted from the class. This means that there will be no record of the class or any letter grade on the student’s transcript. The student’s tuition will not be refunded.
- The Last Day to Withdraw is the last day to withdraw from classes without a grade penalty. Students will receive a grade of W. Students may withdraw from a course through NOVAConnect. The student’s tuition will not be refunded.
Dropping a course after the census date and before the withdrawal date will result in a “W” grade appearing on your transcript. To identify these dates for your courses, please visit the College Academic Calendar and scroll down to the specific session for your course. Please note that any drops or withdrawals from a course may impact financial aid, International Student status, or military benefits. Students with questions should check with the appropriate offices.
Financial Stability and Advocacy Centers
The Financial Stability and Advocacy Centers provide assistance to students who are experiencing financial hardships that might prevent the students’ academic success. The personnel at the Financial Stability and Advocacy Centers work with students to identify college or community services available. For more information, please visit the Financial Stability and Advocacy Centers webpage, or contact the office by calling 703.323.3450 or emailing financialstability@nvcc.edu.
Office of Wellness and Mental Health
During your time at NOVA, you may experience challenges including struggles with academics, finances, or your personal well-being. NOVA has support resources available. Please contact the Office of Wellness and Mental Health if you are seeking resources and support, or if you are worried about a friend or classmate.
Prerequisite Verification Statement
As noted in the Course Prerequisites Policy, some courses have prerequisite or corequisite requirements that are established to foster a student’s success in the course. Students may not enroll in a course for which they do not meet the prerequisites by the time the course begins or for which they do not simultaneously enroll in any corequisite. Students may be administratively dropped from any course for which they have not met the prerequisite. If a course has a prerequisite, it is the responsibility of the student to ensure completion of this prerequisite course first. Any student needing assistance in determining prerequisite or corequisite requirements can reach out to their faculty member or Campus Academic Division office for support.
Remote Student Support Services
If you need academic assistance or need college services but cannot make it to campus, please review NOVA’s Remote Student Support Services to receive virtual assistance. Services provided include enrollment services, advising, tutoring, and financial aid assistance.
Title IX
Title IX is a civil rights law that prohibits discrimination on the basis of sex in educational programs, activities, admission, and employment. Complaints of sex-based discrimination, sexual violence, domestic violence, dating violence, and sexual or gender-based harassment are governed by the Title IX Policy. For more information or to make a report, visit the Office of Title IX.
ITD 140 · Machine Learning I · Section 70YN (CRN 64205) · Northern Virginia Community College