He is very friendly and he explains everything clearly.
07/19/2025
10
/10
Major:
M.S. Computer Information Systems - Artificial Intelligence
AIML-500 Machine Learning Fundamentals
Instructor:
Dr. Sandra Metzger
Delivery:
online
10
/10
The Artificial Intelligence and Machine Learning (AIML) course at Indiana Wesleyan University offers students a compelling deep dive into one of the most transformative fields in computer science. As technology continues to shape every aspect of our lives, understanding the mechanisms behind intelligent systems and learning algorithms becomes essential. This course, designed to introduce and cultivate foundational and advanced concepts in AI and ML, stands as a pivotal learning experience—particularly for students preparing for careers in software engineering, data science, or academic research.
📚 Class Quality and Academic Structure
The quality of this class is characterized by its blend of theoretical depth and practical application. From the outset, the course engages students with a clear outline of learning outcomes, including an introduction to supervised and unsupervised learning, reinforcement learning, and basic neural networks. The curriculum is thoughtfully organized, ensuring that even students who are new to the subject can follow along while still challenging those with prior experience.
Lectures are enriched with up-to-date examples and case studies that bring abstract concepts to life. Whether exploring how spam detection models work or studying the ethical implications of generative AI, the class maintains a consistent relevance to real-world applications. Additionally, students are encouraged to participate in discussions, which fosters a collaborative learning atmosphere where differing perspectives are welcomed and explored.
📝 Homework Load and Academic Expectations
The homework load in this course is rigorous but manageable. Weekly assignments challenge students to apply their understanding through Python and Java programming exercises, algorithm design, and written reflections. Assignments range from solving classification problems with decision trees to building simple recommendation systems. This ensures that students gain both theoretical mastery and coding fluency, an essential combination in the AIML domain.
At times, the workload intensifies—especially during midterms and project weeks—but the assignments are spaced out with consideration. Late submissions are generally discouraged except under approved circumstances, promoting a disciplined approach to time management. The professor frequently provides feedback that’s not just corrective but also instructive, helping students grow and refine their problem-solving skills.
🎓 Classroom Discipline and Participation
Discipline within the classroom environment reflects the university’s broader ethos of respect and personal responsibility. The professor sets clear expectations for conduct from day one. Phones must remain silent, laptops are used solely for academic tasks, and collaboration is governed by a strong academic honesty policy. These standards not only maintain focus during lectures but also foster an atmosphere conducive to meaningful dialogue and inquiry.
Students are held accountable for their participation, not just through attendance but also engagement during sessions. Group activities and discussions are common, encouraging students to build both technical reasoning and communication skills.
🕐 Attendance Requirements and Policy
Attendance in this course is critical and taken seriously. AI and ML are subjects where each lecture builds incrementally on the last, so missing class can leave a noticeable gap in understanding. The university has a well-defined attendance policy, and in this class, students are allowed a limited number of absences before consequences such as grade deductions apply.
The professor emphasizes the importance of being present—not just physically but intellectually. Absences are typically excused only in cases of documented illness, emergencies, or university-sanctioned events. That said, lecture materials and recordings are sometimes made available, allowing students to review content asynchronously if needed.
👨🏫 Professor’s Teaching Style and Pedagogical Approach
One of the most notable strengths of this course is the professor’s teaching style. A blend of enthusiasm, clarity, and rigor defines each session. Rather than simply lecture, the professor frequently incorporates interactive components—live coding demonstrations, visual diagrams (especially with tools like PlantUML), and real-time algorithm tweaking. This dynamic approach makes learning feel more exploratory than didactic.
The professor also appreciates the diverse backgrounds of students, often tailoring examples to suit a range of familiarity levels. Novices feel welcomed, and advanced students are given the opportunity to tackle extra credit challenges or lead small peer discussions. Office hours are well-utilized, with the professor showing genuine commitment to student success outside the classroom.
Furthermore, adaptive learning principles are embedded in the course design. Assessments are designed to identify gaps in understanding, which are then addressed through targeted follow-ups or in-class reviews. This responsiveness elevates the course from good to truly impactful.
🌟 General Evaluation and Conclusion
Overall, the AIML class at Indiana Wesleyan University earns high marks across the board. The class is well-structured, the workload is stimulating without being overwhelming, discipline is upheld in a way that enhances learning, and the professor delivers content with both expertise and compassion. As AI continues to evolve—from language models like transformers to applications in mental health analysis—the course instills not just knowledge but curiosity.
Students emerge from the course not only with a firm grasp of AI principles but also with the skills to apply them thoughtfully and ethically. Whether one aims to pursue a career in software design, data science, or research, this class lays down the groundwork confidently and completely. It exemplifies what higher education should be: rigorous, relevant, and inspiring.
07/19/2025
10
/10
Major:
M.S. Computer Information Systems - Artificial Intelligence
AIML-505 Large Language Models and Generative Artificial Intelligence
Instructor:
Asabe Dawudu
Delivery:
onsite
10
/10
Very good
07/18/2025
10
/10
Major:
M.S. Computer Information Systems - Artificial Intelligence
AIML-505 Large Language Models and Generative Artificial Intelligence
