This course offers students a new perspective on the study of Artificial Intelligence (AI) concepts. The essential topics and theory of AI are presented, but it also includes practical information on data input and reduction as well as data output (i.e. algorithm usage). In particular, this course emphasizes on theoretical and practical aspects of various search algorithms, knowledge representations, and machine learning methods. The course features practical implementations through assignments undertaken both individually and in groups.
🔥Download Course Information here.
- Apply the fundamentals and concepts of AI using various types of AI solutions including search algorithms, knowledge representation, and machine learning methods.
- Formulate the appropriate AI solutions using a selected method based on the problem given.
- Apply the appropriate solutions in AI to solve real problems in the project.
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Student Information
- 🧑💻 Section 07
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Weekly Task
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Lecture Note
- 🔥 Topic 1
- 🔥 Topic 2 Part 1
- 🔥 Topic 2 Part 2
- 🔥 Topic 3 Part 1
- 🔥 Topic 3 Part 2
- 🔥 Topic 6
- 🔥 Topic 7
- 🔥 Topic 8
- 🔥 Revision for FE
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Sample of Questions
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Assignment 1
- 🔥 Submission
- 🔥 Sample of A1
- 🔥 Group 2
- 🔥 Group 3
- 🔥 Group 4
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Assignment 2
- 🔥 Submission
- 🔥 Group 2
- 🔥 Group 3
- 🔥 Group 4
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Assignment 3
- 🔥 Submission
- 🔥 Group 2
- 🔥 Group 3
- 🔥 Group 4
- 🔥 Project Guideline
- 🔥 Competition
- 🔥 Ebook
- 🔥 Submission
Week | Dates | Topic | Content |
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1 | 8-14 Oct | Computer and Intelligence | Introduction to thinking, computer architecture, and intelligence, What is artificial intelligence (AI), AI timeline and current trend, Responsible AI, Key Workload AI, Artificial Intelligence in Microsoft Azure, Computational Intelligence, AI Applications, AI Applications and IR 4.0. |
2-3 | 15 - 28 Oct | Knowledge Representation | What is knowledge representation, Importance of representing knowledge, Syntax and semantics, Propositional logic, Predicate logic, Inference process, Proof procedure. Project & Assignment Briefing |
4 | 29 Oct - 4 Nov | Search Algorithms | Simplified Graph Theory (Structure for Problem-Solving), Exhaustive search algorithms, Breadth-first search, Depth-first search |
5 | 5-11 Nov | Search Algorithms (BFS & DFS) | Simplified Graph Theory (Structure for Problem Solving), Exhaustive search algorithms, Breadth-first search, Depth-first search. Quiz 1. A1 Submission |
6-7 | 12 Nov - 25 Nov | Search Algorithms (Heuristic Algorithm) | Heuristic search algorithm, Heuristic evaluation and best first search (including A* search), Evaluation criteria (admissibility, monotonicity, and informedness). Mid-Term Test (22 Nov 2023 8 pm - 10 pm). A2 Kick-off |
8 | 26 Nov - 2 Dec | MID SEMESTER BREAK | |
9 | 3-9 Dec | Problem-Solving with Search (Minimax and Alpha-Beta Pruning) | Game playing (minimax and alpha-beta), Search engine, social media, and bots. A2 Submission, A3 Kick-off, Peer Review Part 1 |
10 | 10-16 Dec | Search Planning and Control | Recursion based search, Pattern-based search |
11-12 | 17 Dec - 30 Dec | Advanced Artificial Intelligence | Agent and distributed-based search, Smart computing applications, Natural Language Processing Application, Computer Vision. Quiz 2. A3 Submission, Project Kick-off |
13-14 | 31 Dec - 13 Jan | Machine Learning | Overview of machine learning, Supervised vs unsupervised learning, Classification, clustering, reinforcement, and regression, Machine Learning in Microsoft Azure Framework, Anomaly Detection |
15 | 14-20 Jan | Project Demo, Peer Review Part 2 | |
16-18 | REVISION WEEK AND FINAL EXAM |