Topic: Clustering K-Means, Learning from Observation This week we reviewed about supervised, unsupervised and reinforcement learning. After the short review, we got into a part of the main material which is clustering techniques. We learned that clustering techniques is a … Continue reading →

Topic: Uncertainty Reasoning This session we discussed supervised vs unsupervised learning, and the parameters they require. We also learned the different types of formal languages such as fuzzy logic, propositional logic, etc. We also learned how to represent probability based … Continue reading →

Topic: Adversarial Search This session we discussed the concept of adversarial search, which is an algorithm which puts opponents into consideration when deciding the best possible move for a game. We went in more depth about the minimax principle, which … Continue reading →

Topic: Informed Search & Local Search This week we discussed about informed search, which includes algorithms like the A* algorithm and the Greedy Best First Search. We learned how to assign heuristic values, what they mean and how the algorithms … Continue reading →

Topic: Uninformed Search This week we discussed multiple uninformed search algorithms, which are also known as blind algorithms. There are 5 algorithms we learned, which consists of BFS, DFS, UCS, DLS and IDS. The algorithms that we focused on in … Continue reading →

The first topic of the semester was that about the introduction to the course, final project requirements and basic concepts about AI and machine learning. Despite only hearing about AI and machine learning only in the recent years, I had … Continue reading →