Advance Course in Applied Computing with AI

Pre-requisites

• Basic programming knowledge
• Flowcharts

1. Introduction to Java

   • JVM Architecture
   • Primitive data types
   • OOP Concepts using Java
   • Interfaces
   • Arrays
   • Garbage collection
   • Inner Class
   • Wrapper Classes and String Class
   • Exception Handling
   • java.io & java.nio Packages
   • Object Class & java.util Package
   • Collections
   • MultiThreading
   • Synchronization
   • Lambda Expressions

2. Problem Solving & Computational Thinking

3. Algorithms & Data Structures

   • Basic Data Structures
   • Linked List Data Structures
   • Recursion
   • Trees & Applications
   • Searching Algorithms
   • Sorting Algorithms
   • Hash Functions & Hash Tables
   • Graph & Applications
   • Algorithm Designs
   • Analysis of different types of Algorithms
   • Data Structure Implementation and Applications

Pre-requisites:

Basic computer knowledge

• Database Management Systems (DBMS)

  • RDBMS (Relational Database Management Systems)
  • Database Design
  • Entity-Relationship Diagram
  • Codd’s 12 Rules for RDBMS
  • Normalization
  • Data Types
  • Database Constraints

• Structured Query Language (SQL)

  • Categories of SQL Commands
  • SQL Functions & Operators
  • Joins
  • Subquery
  • Views & Indexes
  • ACID Properties
  • Stored Procedures
  • Cursors
  • Triggers

• Introduction to NoSQL

Pre-requisites:

Pre-requisites:

• Must have completed Java Object-Oriented Programming & Data Structure
• Must have completed Database Technologies and SQL
• Must have completed Web Technologies

1. .NET Framework

   • Visual Studio
   • C# Basics
   • Interfaces & Indexers
   • Generic Classes
   • Collections
   • Delegates
   • Lambdas
   • Exception Handling
   • LINQ to Objects
   • PLINQ
   • Files I/O and Streams
   • Threading

2. ASP.NET MVC

   • MVC State Management
   • MVC Module
   • Data Management with ADO.NET
   • Understanding Routing & Request Life Cycle
   • Layouts
   • Bundle
   • Minification
   • MVC Security
   • Entity Framework
   • Understanding ASP.NET MVC Core

3. Windows Communication Foundation

4. Web APIs

5. Git

6. Software Engineering

   • Software Development Life Cycle
   • Object-Oriented Analysis and Design
   • Agile Development Model
   • Introduction to Atlassian Jira

7. Microservices

   • API Gateway

8. DevOps

   • Containerization
   • Docker
   • YAML
   • Kubernetes

9. Software Testing

   • Software Testing Life Cycle (STLC) and V Model
   • Manual & Automation Testing
   • Selenium
   • Jenkins

1. Introduction to Artificial Intelligence

2. Programming Crash Course

   • Python: Basics, Loops, Blocks and Statements, Lists, Sets, File I/O, Modules and Functions, OOP Concepts
   • Keras: Introduction, Installation, and Configuration
   • TensorFlow: Tensors, TensorFlow Installation, TensorFlow Basics

3. Data Analytics

   • Big Data Analytics: Expand coverage to include big data analytics concepts and technologies such as Apache Hadoop, Apache Spark, and distributed computing frameworks for processing large-scale datasets.

4. Mathematics for AI

   • Probability Theory and Statistics: Expand coverage of probability theory and statistical methods relevant to AI, including Bayesian inference, statistical hypothesis testing, and probabilistic graphical models.

5. Deep Learning

   • Introduction to Neural Networks
   • Deep Neural Networks
   • Perceptrons
   • RNN (Recurrent Neural Networks)
   • CNN (Convolutional Neural Networks)
   • LSTM (Long Short-Term Memory)
   • Deep Belief Network
   • Semantic Hashing
   • Building Deep Learning Models/Neural Networks using Keras and TensorFlow
   • Testing and Training the Models

6. Convolutional Neural Networks

7. Natural Language Processing & Computer Vision

   • Multimodal Learning: Explore methods for combining text, image, and other modalities in AI applications, such as multimodal embeddings, fusion architectures, and cross-modal retrieval.
   • Ethical Considerations in AI Applications

8. AI Compute Platforms, Applications & Trends

Pre-requisites:

• Must have completed any 4 certificates from CCDAC101 to CCDAC105.

In addition to the specific subject knowledge, the Software Project module attempts to put into practice several concepts that the students have learned during the course, such as:

• Ability to work in a team
• Software development methodology and principles
• Good programming practices
• Technical reporting and presentation

The Software Project module is divided into three phases:

1. SRS Phase

   • Tasks: Requirements gathering, feasibility study, and project planning.

2. Design Phase

   • Tasks: Software design and project plan development.

3. Development Phase

   • Tasks: Coding and testing of the software system/application.