Course Title: Artificial Intelligence and Machine Learning Laboratory
Type of Course: Optional, Sessional
Offered to: EEE
Pre-requisite Course(s): None
The sessional course will be conducted in two parts. In the first part of the sessional course, the students will perform experiments in relevance with the EEE 401 course. In the second part of the course, the students will perform design projects related to EEE 401 course contents to achieve specific program outcomes.
To perform experiments in relevance with the theoretical concepts of the course EEE 401: Artificial Intelligence and Machine Learning
To conduct design projects in order to achieve specific program outcomes described in the Course Outline
Fundamental understanding of concepts of Mathematics courses.
| CO No. | CO Statement | Corresponding PO(s)* | Domains and Taxonomy level(s)** | Delivery Method(s) and Activity(-ies) | Assessment Tool(s) |
|---|---|---|---|---|---|
| CO1 | understand different AI and machine learning algorithms and use programming software to implement them | PO(a), PO(e) | P1, P4 | Lectures, Lab work, Lab test |
Lab Performance Lab Report Lab Test Quiz |
| CO2 | solve real-life problems by using AI and machine learning based algorithms | PO(d) | C4, C5 | Lectures, Lab work Lab test |
Lab Performance Lab Report Lab Test Quiz |
| CO3 | analyze real life challenges in implementing supervised and unsupervised learning algorithms | PO(c) | C4, C5 | Lectures, Lab work Lab test |
Lab Performance Lab Report Lab Test Quiz |
| CO4 | demonstrate application of ethical principles and practices in the project, and evaluate peer team members ethically | PO(h) | A3 | -- | Peer evaluation, Report |
| CO5 | work effectively as an individual and as a team member towards the successful completion of the project | PO(i) | P4 | -- | Viva, Peer evaluation |
| CO6 | report effectively on the design done for CO4 with presentation, user-manual and detailed report | PO(j) | A3 | -- | Video Presentation Project Report |
Cognitive Domain Taxonomy Levels: C1 – Knowledge, C2 – Comprehension, C3 – Application, C4 – Analysis, C5 – Synthesis, C6 – Evaluation, Affective Domain Taxonomy Levels: A1: Receive; A2: Respond; A3: Value (demonstrate); A4: Organize; A5: Characterize; Psychomotor Domain Taxonomy Levels: P1: Perception; P2: Set; P3: Guided Response; P4: Mechanism; P5: Complex Overt Response; P6: Adaptation; P7: Organization
Program Outcomes (PO): PO(a) Engineering Knowledge, PO(b) Problem Analysis, PO(c) Design/development Solution, PO(d) Investigation,
PO(e) Modern tool usage, PO(f) The Engineer and Society, PO(g) Environment and sustainability, PO(h) Ethics, PO(i) Individual work and team work,
PO(j). Communication, PO(k) Project management and finance, PO(l) Life-long Learning
* For details of program outcome (PO) statements, please see the departmental website or course curriculum
| K1 | K2 | K3 | K4 | K5 | K6 | K7 | K8 | P1 | P2 | P3 | P4 | P5 | P6 | P7 | A1 | A2 | A3 | A4 | A5 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Week | Delivery | Topic |
|---|---|---|
| 1 | Introduction and Expt.-1 (A) | Introduction to fundamentals of artificial intelligence and machine learning and their major applications Overview on lab experiments, projects, policies, grading; group formation |
| 2 | Expt. 1 (B, C) | Implementation of Python basic libraries Performing basic tasks using Python programming, data handling, statistical operations, data reshaping, filtering, merging, handling missing values. Implementation of basic AI operations |
| 3 | Expt.- 2 | Implementation of Breadth First Search (BFS), A* Search and Tree Search algorithm Implement BFS in Tic-Tac-Toe problem or Robot Grid Movement |
| 4 | Project Proposal Presentation |
Project proposal, discussion on overall outcome of the project, technical requirement, task distribution among the group members |
| 5 | Expt.- 3 | Implementation of KNN and Kmeans algorithm and test with a dataset. |
| 6 | Expt.- 4 | Implementation of linier regression and logistic regression algorithms and test with a dataset. |
| 7 | Project Design Presentation | Present/demonstrate the technical progress of the project Literature review, data collection, algorithm development, discussion on preliminary findings Describe contextual knowledge to assess societal, health, safety, legal and cultural issues relevant to the project |
| 8 | Expt.- 5 | Implementation of support vector machine algorithm and test with a dataset. |
| 9 | Expt.- 6 | Implementation of simple convolutional neural network (CNN) architecture and test with a dataset. |
| 10 | Project Progress Presentation | Present/demonstrate the technical progress of the project Describe any necessary modification proposed to address public health and safety, cultural, societal, and environmental considerations related to the project Evaluate the limitations of the technology used in the project Present the draft project report and draft presentation |
| 11 | Quiz and Lab Test | Quiz and Lab Test based on Experiment 1-5 |
| 12 | Peer Assessment and Vivat | Present/demonstrate the technical progress, team and individual contribution and ethical principles applied to the design and implementation of the project Answer Technical Questions related to the project Individually and ethical principles applied to the design and implementation of the project Complete the Peer Assessment Survey to ethically evaluate the contribution to the project individually and as a team |
| 13 | Project Demonstration | Use multimedia and necessary documentation (user manual, video demonstration and project report) to clearly communicate the project Participate in the project showcase and communicate the design to industry stakeholders |
Class participation will be judged by in-class evaluation; attendance will be recorded in every class.
Continuous assessment will be done in the form of laboratory tasks, assignments, laboratory-tests, report writing and viva.
A group project on the design of a digital system performing a specific task with the help of various signal processing operations has to be completed by the end of this course following the detailed guideline. A project report has to be submitted as per the instructions and the project has to demonstrated and presented in the class for evaluation.
Instructions on Lab Project
Students are to demonstrate the culmination of Course Outcomes through a small project, that can be implemented in roughly 5 Weeks. A Project Proposal needs to be prepared by the student group.
Project Requirements:
Must have conflicting / wide range solution (say improving speed of a circuit might also increase power consumption) (P(a))
Must be an open-ended real-life problem with no obvious solution (P(b)) (Complex Engineering problem)
Project should address community needs, public health and safety, cultural, societal, and environmental considerations [CO3 (PO(c))]
Project must involve real-life data and its necessary processing using software. Understand the limits of the used technology. [CO2 (PO(e))]
Evaluation
10 Minutes recorded video presentation [with PPT slides] [CO6 (P(j))]
Peer Evaluation of Group Members [CO4 (PO(h))], [CO5 (PO(i))]
Report in prescribed format with:
Literature survey on concerned technology [CO4 (PO(l))]
Technical Details of the Solution [CO6 (PO(j))]
Teamwork and Individual Performance Report [CO5 (PO(i))]
Technological Limit Evaluation [CO2 (PO(e))]
Public health and safety, cultural, societal, and environmental considerations [CO3 (PO(c))]
Ethics declaration statement [CO4 (PO(h))]
Lab Reports and Lab Performance 10%
Lab test/Viva/Quiz 40%
*Assessment will be performed by internal and external evaluators with industry experience
* marks distribution of the project will be declared at the beginning of the semester
Artificial Intelligence: A Modern Approach by Stuart Jonathan Russell and Peter Norvig.
Kernel Methods and Machine Learning by Sun Yan Kung
Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville.
N.B. Besides going through relevant topics of the textbook, it is strongly advised that the students follow the lab manuals, class Lectures and discussions regularly for a thorough understanding of the topics.