Indiana Wesleyan University

Professor Paola Saibene has made a very positive first impression. Although the course has just started, I have really enjoyed the class so far. She explains concepts in a clear and organized way and is always willing to answer questions, which makes it easier to follow the material and stay engaged. The workload seems appropriate for a graduate-level course. The assignments are challenging enough to reinforce what we learn in class without feeling overwhelming. So far, the expectations have been well communicated, which has helped me understand what is expected throughout the course. Professor Saibene encourages participation and creates a welcoming classroom environment where students feel comfortable asking questions and sharing their ideas. I also appreciate that she takes the time to clarify concepts whenever someone needs additional explanation instead of simply moving on. Attendance seems important because each class builds on previous discussions and activities. Being present allows students to better understand the material and benefit from the classroom interactions. Overall, my experience has been very positive so far. Even though we are only at the beginning of the semester, I appreciate Professor Saibene's teaching style and her willingness to support students. I am looking forward to the rest of the course and would recommend taking a class with her based on my experience so far.

Professor Brayton has made a very positive first impression. Although the course has just started, I have enjoyed the class so far. He explains the material clearly and takes the time to answer students' questions, making the learning environment comfortable and engaging. The workload seems reasonable for a graduate-level course. The assignments appear to reinforce the concepts covered in class without feeling overwhelming. So far, the expectations have been clear, which has helped me stay organized. Classroom participation is encouraged, and Professor Brayton creates an atmosphere where students feel comfortable asking questions and sharing their thoughts. He is patient when explaining concepts and makes sure everyone understands before moving on. Attendance seems important because each class builds on previous discussions and activities. Being present allows students to participate, ask questions, and benefit from the explanations provided during class. Overall, my experience has been very positive so far. Even though we are only at the beginning of the course, I appreciate Professor Brayton's teaching style and his willingness to help students understand the material. I look forward to learning more throughout the semester and would recommend this course based on my experience so far.

I am currently pursuing the MS in Artificial Intelligence – Data Analytics, and I recently started the course AIML-505: Large Language Models and Generative Artificial Intelligence with Professor Melisa Snyder. Since this review is based on the beginning of the course, my evaluation focuses on the initial course structure, first impressions, expectations, and early learning experience rather than final outcomes. At the start of the course, AIML-505 appears to be a highly relevant and valuable class for students in the Artificial Intelligence and Data Analytics major. The subject matter is especially important because large language models and generative AI are becoming central to many areas of technology, business, analytics, automation, and decision-making. The course seems designed to help students understand not only how these models work but also how they can be applied responsibly in real-world situations. The early course materials give a clear indication that students will be expected to think critically about AI tools, model behavior, prompt design, limitations, and practical applications. The quality of the class so far seems strong. The course content is organized in a way that introduces students to important concepts step by step. Since this is the start of the course, the material feels challenging but manageable. The topics are modern and directly connected to current developments in artificial intelligence, which makes the class engaging. For a student in the MS in Artificial Intelligence – Data Analytics program, this course feels useful because it connects technical AI knowledge with analytical thinking and practical problem-solving. The homework load at the beginning of the class appears reasonable, but it also requires consistent attention. Students should expect to spend time reading course materials, understanding concepts, completing assignments, and applying ideas through written or practical work. The workload does not seem overwhelming at the start, but it is clear that students need to stay organized and avoid falling behind. Because generative AI is a broad and fast-moving topic, completing assignments carefully will likely require both independent study and thoughtful reflection. Classroom discipline and participation expectations also seem important. Whether the course is taken online or onsite, students are expected to remain engaged, follow instructions, meet deadlines, and participate professionally. Attendance requirements appear to be an important part of the learning process because the early sessions and course introductions help students understand expectations, assignment structure, and how the subject will be developed throughout the term. Being present and attentive at the beginning of the course is especially valuable because it sets the foundation for later topics. Professor Melisa Snyder’s teaching style, based on the start of the class, appears structured, supportive, and focused on helping students understand complex AI concepts in a clear way. The course expectations are presented professionally, and the teaching approach encourages students to connect theory with practical applications. This is helpful for students who may have different levels of prior experience with artificial intelligence or generative AI tools. Overall, my initial impression of AIML-505 is positive. The course appears to be well-aligned with the MS in Artificial Intelligence – Data Analytics major and provides a strong foundation in one of the most important areas of modern AI. At this early stage, I believe the class will be valuable for developing both technical understanding and practical awareness of large language models and generative artificial intelligence.

Feedbacks are a little slow but overall good communication and good teaching provided by professor.

Good professor. Makes an online class interesting to learn.

DTAN-505 Data Visualization with Dr. Frank Zeng was a valuable and well-structured course for the MS in Artificial Intelligence – Data Analytics program. The class helped me better understand how data visualization is not just about creating charts, but also about communicating insights clearly, accurately, and professionally. Dr. Zeng explained the concepts in a practical way and connected the course material to real-world data analytics situations, which made the learning experience meaningful. The quality of the class was very good. The topics were organized logically, and the assignments helped reinforce what we learned in class. The homework load was manageable and appropriate for a graduate-level course. Some assignments required careful effort, especially when working with datasets and visualizations, but they were useful in developing hands-on skills. The workload encouraged consistent learning without feeling overwhelming. Dr. Zeng’s teaching style was clear, patient, and supportive. He explained important visualization techniques and encouraged students to think about how to present data effectively for decision-making. He also emphasized accuracy, design, and interpretation, which are important skills for students pursuing data analytics and artificial intelligence careers. The classroom discipline and expectations were professional. Attendance and participation were important because the class discussions and explanations helped in understanding the assignments better. Dr. Zeng maintained a respectful learning environment and encouraged students to stay engaged. Overall, I had a positive experience in this course. DTAN-505 improved my confidence in data visualization and helped me understand how to transform raw data into meaningful visual stories. I would recommend this course to students who want to strengthen their analytical, technical, and communication skills in data analytics.

Professor provides timely feedback which helps us in achieving better knowledge and understanding of the course.

After completing the first two weeks of DTAN 505, I have had a positive learning experience overall. The course is well organized and provides a structured introduction to important concepts related to data analytics and practical problem-solving. Even within the first two weeks, the class has covered meaningful topics that help students understand how analytics can be applied in real-world business and technology environments. One of the strongest aspects of the course is the quality of instruction provided by Frank Zeng. Professor Zeng explains concepts in a clear and organized manner, making the material easier to understand for students with different technical backgrounds. His teaching style is practical and focused on helping students connect theoretical ideas with real-world applications. During lectures, he often provides examples and scenarios that help clarify difficult concepts and encourage students to think critically about how data analytics is used in professional settings. The classroom environment has been professional and well managed. There is a clear expectation that students remain attentive, participate in discussions, and maintain respectful classroom behavior. Professor Zeng encourages interaction and questions during class, which creates a collaborative learning atmosphere. Students are comfortable asking for clarification, and discussions often help deepen understanding of the topics being covered. Attendance requirements are taken seriously in this course. Since the material builds progressively from one topic to another, attending class regularly is important for staying engaged and understanding the lessons. Participation during lectures and discussions also contributes to the overall learning experience. Missing sessions could make it more difficult to follow future topics because the concepts are interconnected. The homework load so far has been moderate and manageable. Assignments are designed to reinforce the concepts discussed during lectures and encourage students to apply analytical thinking to practical problems. The workload requires consistent effort, but it is not overwhelming. Students who review the lecture materials regularly and stay organized should be able to manage the assignments effectively. Another positive aspect of the course is that the assignments are meaningful and connected to the course objectives. Rather than focusing only on memorization, the homework encourages students to analyze information, think critically, and apply problem-solving techniques. This approach helps students build confidence and gain practical understanding of analytics concepts. Professor Zeng’s teaching style is supportive and student-focused. He explains topics patiently and ensures that students understand the material before moving on to more advanced concepts. He also provides useful feedback and guidance that helps students improve their understanding and performance. His approachable teaching style contributes to a positive classroom experience and encourages active learning. The pace of the course during the first two weeks has been steady and manageable. The lessons are structured in a logical sequence, which helps students gradually build a foundation in the subject. Even students who may not have extensive prior experience in analytics can follow along and learn effectively through the lectures, discussions, and assignments. Overall, DTAN 505 has provided a strong start and appears to be a valuable course for students interested in data analytics and related fields. The combination of organized instruction, practical assignments, professional classroom discipline, and interactive discussions creates a productive learning environment. The course successfully balances theory and practical application, which helps students understand not only the concepts themselves but also how they can be used in real-world situations. In conclusion, my experience during the first two weeks of DTAN 505 has been positive. The course content is relevant and engaging, the homework load is reasonable, and the classroom environment supports learning and participation. Professor Frank Zeng’s clear teaching style and practical approach make the material accessible and interesting. Overall, the course provides a strong academic experience and a solid foundation for further learning in data analytics.

AIML 500 – Machine Learning Fundamentals provided a strong introduction to the fundamental concepts and applications of machine learning. The course was conducted onsite from March 5–7 at the Merrillville campus in rooms 220/221 and offered an intensive learning experience that combined theoretical explanations with practical discussions. Overall, the course was well organized and provided students with a clear understanding of how machine learning techniques are applied in modern data-driven environments. One of the strengths of this course was the quality of instruction delivered by Clifford Birdsell. Dr. Birdsell’s teaching style was clear, engaging, and focused on helping students understand the underlying principles behind machine learning rather than simply memorizing concepts. He explained topics in a structured way, starting from the basic foundations and gradually building toward more advanced ideas. This approach made the material accessible even to students who may not have extensive prior experience in artificial intelligence or machine learning. Dr. Birdsell frequently used real-world examples and practical scenarios to illustrate machine learning concepts. These examples helped students understand how algorithms and data analysis techniques are used in industries such as technology, business analytics, and research. His ability to simplify complex ideas and connect them to real-world applications made the lectures both informative and engaging. Additionally, he encouraged students to ask questions and participate in discussions, which helped create an interactive learning environment. The overall quality of the class was high. The course content covered essential machine learning fundamentals such as data preparation, basic algorithms, model evaluation, and the role of machine learning in decision-making processes. The structure of the course allowed students to gain both conceptual knowledge and a broader understanding of how machine learning fits into the larger field of data analytics and artificial intelligence. Since the course was delivered in an intensive onsite format over three days, the learning experience was focused and immersive. Students were able to concentrate on the material during the sessions without long gaps between topics. This format allowed for continuous engagement with the course content and helped reinforce the concepts discussed during the lectures. The homework and assignment load for the course was reasonable and manageable. The assignments were designed to reinforce key concepts introduced during the lectures and encourage students to apply their understanding to practical scenarios. Rather than overwhelming students with excessive workload, the course assignments focused on strengthening analytical thinking and helping students become more comfortable with machine learning concepts. Another important aspect of the course was the emphasis on classroom discipline and professionalism. Students were expected to maintain a respectful learning environment, arrive on time, and remain attentive during lectures and discussions. Because the class sessions were intensive and condensed into a few days, maintaining focus and discipline was important for ensuring that all students could benefit from the lectures and collaborative discussions. Attendance requirements were clearly communicated and taken seriously. Since the course was delivered onsite and covered multiple topics in a short period of time, attending each session was essential for understanding the progression of the material. Missing even a portion of the class could make it more difficult to follow later discussions, so consistent attendance was encouraged. The structured schedule ensured that students remained engaged throughout the learning sessions. Another positive aspect of the class was the collaborative learning environment. Students were able to exchange ideas, ask questions, and discuss different perspectives related to machine learning and artificial intelligence. This interaction contributed to a more dynamic classroom experience and allowed students to learn not only from the instructor but also from their peers. In terms of overall evaluation, AIML 500 – Machine Learning Fundamentals is an effective introductory course for students interested in artificial intelligence and machine learning. The course successfully introduces key concepts while maintaining a balance between theory and practical understanding. The intensive onsite format provides an immersive learning experience, and the structured teaching approach helps students build a solid foundation in machine learning principles. In conclusion, AIML 500 is a valuable course for students beginning their journey in machine learning. The quality of instruction, clear course structure, manageable assignments, and interactive classroom environment all contribute to a positive academic experience. Dr. Birdsell’s teaching style helps students understand complex machine learning concepts in a clear and practical way, making the course both informative and engaging. Overall, the course provides a strong foundation for further study in machine learning, data analytics, and artificial intelligence.