/Artificial-Intelligent

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).

Primary LanguageJupyter Notebook

Artificial-Intelligent

WBDM

Course Synopsis

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.

Course Learning Outcomes

  1. Apply the fundamentals and concepts of AI using various types of AI solutions including search algorithms, knowledge representation, and machine learning methods.
  2. Formulate the appropriate AI solutions using a selected method based on the problem given.
  3. Apply the appropriate solutions in AI to solve real problems in the project.

🔥 Important things

  1. Microsoft Azure AI Fundamentals: AI 900

  2. Student Information

  3. Weekly Task

  4. Lecture Note

  5. Sample of Questions

  6. Assignment 1

  7. Assignment 2

  8. Assignment 3

  9. Project - Prototype Development & Competition

Weekly Schedule

Week Dates Topic Content
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

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