Please note that this evaluation is based on the average score for each reviewer in all the criteria

• The number of reviewers: 5

Graphs

<aside> 💡 Short Description of the Course Material:

Introduction to AI; Agents and environments. Uninformed vs. informed search. Constraint satisfaction. Probabilistic inference; conditional probability and independence. Supervised learning using Nearest Neighbor and SVM. Clustering with the mean-shift algorithm. Overview of Neural Networks and training. Overview of deep learning and applications. Feature extraction techniques in Computer Vision. Applications in reinforcement learning. Ethical concerns in AI.

Prerequisite: ISE 291

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General Evaluation

• Difficulty of the course : 2.8/5
• Is there a Lab for this course: No
• is there a project for this course: No
• Are there assignments for this course: Yes
• Is there are any other requirements? (Things that are required from students apart from assignments and projects): No
• in your opinion, How important is the course content for other courses in the plan? : 2.2/5 (Important)

Detailed Evaluation