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5.0
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5.0
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1,000
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99%
Would take again
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| Name | Grades | Rating | |||
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ITSS 4383 Judd Bradbury | |||||
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ITSS 4383 Judd Bradbury | |||||
Grades: 2,946
Median GPA: B+
Mean GPA: 3.290
2.5
Professor rating
3.8
Difficulty
96
Ratings given
30%
Would take again
Advanced Applied Artificial Intelligence/Machine Learning
ITSS 4383 (Same as BUAN 4383)
Naveen Jindal School of Management
This course provides a comprehensive introduction to the practical applications of Artificial Intelligence (AI) and Machine Learning (ML). Students explore key concepts, techniques, and tools used in the industry to solve real-world problems. The curriculum covers supervised and unsupervised learning, neural networks, natural language processing, deep learning, and reinforcement learning. Additionally, the course emphasizes leveraging statistical techniques to build, evaluate, and interpret AI and ML models. Through hands-on projects and case studies, students gain experience in building and deploying AI and ML models. 3 credit hours.
Prerequisites: (ITSS 3312 or BUAN 4381 or ITSS 4381) and (BUAN 4373 or OPRE 4373).
Offering Frequency: Each semester
This professor/course combination hasn't been taught in the semesters you selected. To see more grade data, try changing your filters.
Grades: 0
Median GPA: None
Mean GPA: None
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Grades: 2,946
Median GPA: B+
Mean GPA: 3.290
2.5
Professor rating
3.8
Difficulty
96
Ratings given
30%
Would take again
Advanced Applied Artificial Intelligence/Machine Learning
ITSS 4383 (Same as BUAN 4383)
Naveen Jindal School of Management
This course provides a comprehensive introduction to the practical applications of Artificial Intelligence (AI) and Machine Learning (ML). Students explore key concepts, techniques, and tools used in the industry to solve real-world problems. The curriculum covers supervised and unsupervised learning, neural networks, natural language processing, deep learning, and reinforcement learning. Additionally, the course emphasizes leveraging statistical techniques to build, evaluate, and interpret AI and ML models. Through hands-on projects and case studies, students gain experience in building and deploying AI and ML models. 3 credit hours.
Prerequisites: (ITSS 3312 or BUAN 4381 or ITSS 4381) and (BUAN 4373 or OPRE 4373).
Offering Frequency: Each semester
This professor/course combination hasn't been taught in the semesters you selected. To see more grade data, try changing your filters.
Grades: 0
Median GPA: None
Mean GPA: None
Click a checkbox to add something to compare.