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Admission deadline: (Batch 30)
2024-10-30

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Course Detail:

Course Name: Introduction to Artificial Intelligence

Introduction

The Artificial Intelligence course at the Information Access Center (IAC) is designed for professionals aiming to delve into the fascinating world of AI, covering essential concepts, machine learning, and neural networks. Participants will gain hands-on experience with Python, scikit-learn, TensorFlow, and other AI frameworks.

Objectives

To provide an introduction to the fundamentals of Artificial Intelligence and its applications.
To equip participants with essential Python programming skills for AI development.
To introduce various machine learning algorithms, including linear regression, logistic regression, decision trees, and XGBoost.
To explore advanced topics such as neural networks, convolutional neural networks (CNN), and recurrent neural networks (RNN).
To demonstrate practical implementation through assignments and projects in AI.

Outcome of the Learning

Upon successful completion of the Artificial Intelligence course, participants will be able to:

Understand the fundamentals and applications of Artificial Intelligence.
Master Python programming basics, including variables, conditional statements, loops, and object-oriented programming.
Apply machine learning algorithms such as linear regression, logistic regression, decision trees, and XGBoost.
Explore advanced topics in AI, including neural networks, CNN, and RNN.
Implement practical AI solutions using scikit-learn, TensorFlow, Keras, and other frameworks.
Complete assignments and projects that demonstrate proficiency in AI concepts and applications.

Prerequisite

We expect that you have prior programming knowledge with at least one programming language. If you do not have any prior programming knowledge, please learn Python Programming before starting this course. You can participate our Python Bootcamp to learn the basics of Python. Python Bootcamp Link (https://cse.buet.ac.bd/iac/course_detail/IAC-AI-IAI).

Tentative Class Schedule

The course length will be 8 weeks with two classes in each week and 3 hours in each class. The tentative lecture plan of the course is as follows:

Class#	Content
1	Introduction to ML and Python Basics (Environment setup, Variables)
2	Python Basics (Conditional Statement, Loop, Array) Note: This lecture will not be taken if all the participants know the basics of Python. In that case we will explore more on AI.
3	Python Basics (Object Oriented Programming, Function, File) Note: This lecture will not be taken if all the participants know the basics of Python. In that case we will explore more on AI.
4	Python Advanced (Numpy, Pandas)
5	Python Advanced (Matplotlib, Seaborn)
6	Linear Regression
7	Logistic Regression and Assignment 1 on Linear & Logistic Regression Declaration
8	Introduction to scikit-learn, Decision Tree, XGBoost
9	Evaluation of Machine Learning Models (Cross-validation, overfitting, learning curve, over sampling/under sampling, etc.)
10	Assignment 1 Evaluation, Assignment 2 on XGBoost Declaration and Introduction to Neural Networks
11	Introduction to Keras (Tensorflow), Introduction to Deep Neural Network (DNN)
12	Introduction to Convolutional Neural Network (CNN)
13	Handwritten digit recognition using CNN and Assignment 3 on CNN Declaration
14	Introduction to Recurrent Neural Network (RNN) and Transformer based models, What is Large Language Model (LLM)?
15	Implementing Translator using RNN
16	Final Exam and Assignment 2 & 3 Evaluation

Learning and Evaluation Method

Classes will be conducted in a multimedia-equipped environment.
Expert faculty members from the field of Artificial Intelligence will lead each class.
Participants will have access to PCs for practical exercises, ensuring an interactive learning experience.
An evaluation will take place through assignments and a final exam at the end of the course.
We will provide a certificate upon successfully passing this course.

Further Query

Email: iac@cse.buet.ac.bd
Phone: 9665650-80 Ext-6438, mobile : 01741 686742