Overview

Artificial Intelligence and machine Learning (AI&ML) is a new, emerging field which consists of a set of tools and techniques used to extract useful information from data. AI&ML is a fast growing discipline and is full of rigorous practical analysis. The demand for undergraduates in AI and ML has industry required skills and demand in the global market over the last few years. Artificial Intelligence and Machine Learning is also in line demand with computer science. Machine learning is an established research discipline. However, recent advances have increased the impact on many areas of society, science, medicine, and everyday life. AI with ML is in demand in the robotics applications, space technology, industry 4.0 and many more. AI and ML delivers modern computational systems that demonstrate capabilities of perception, reasoning, learning and action that are typical of human intelligence.

Syllabus

Semester I Semester II
Python Statistics
  • Installation of Anaconda
  • Introduction to jupyter notebook
  • Python objects,Data Type, Type casting
  • Python Operators, Conditional Statement, Data Structures
  • Loop Statements, Functions
  • Numpy
  • Pandas
  • Visualization - Seaborn
  • Application of Python in ML
  • Assignments, Assessments & Mentoring
  • Statistics Introduction, Applications, Types of Statistics, Basic terminologies, Types of Data
  • Sampling, Types of Sampling, Cochran's Formula,
  • Sampling Error Measures of Central Tendency, Measure of
  • Spread,Normal Distribution & Properties, Python
  • Implementation
  • Types of HT, Basic Terminologies in HT
  • Introduction and application to ML
  • 2 AI used cases
  • Assignments, Assessments & Mentoring
Semester III Semester IV
Machine Learning - I Machine Learning - II
  • Introduction to ML
  • Linear Regression (MV,Outlier)
  • Logistic Regression (Scaling)
  • SVM (Cross Validation, Categorical Micro Projects
  • Naïve Bayes (Balancing the data)
  • Assignments, Assessments, Mentoring & Micro Projects
  • Clustering
  • KNN
  • Decision Tree (Hyperparameter tuning)
  • Bagging, Random Forest
  • Boosting
  • Introduction to AI
  • 1 AI Used cases
  • Mentoring (2 sessions)
  • Assignments, Assessments, Mentoring & Micro Projects
Semester V Semester VI
Artificial Intelligence Projects
  • AI overview
  • Computer Vision 1
  • Computer Vision 2 - Object Detection
  • Natural Language Processing
  • Mentoring
  • Assignments, Assessments, Mentoring & Micro Projects
  • How to understand and work on real life AI projects
  • 2 AI Projects, paper submission and mentoring

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