Workshop on Machine Learning & AI
Artificial Intelligence (AI, also machine intelligence, MI) is intelligence displayed by machines, in contrast with the natural intelligence (NI) displayed by humans and other animals. In computer science AI research is defined as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of success at some goal. Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using the rules to reach approximate or definite conclusions), and self-correction. Particular applications of AI include expert systems, speech recognition and machine vision.
Machine Learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can change when exposed to new data. The process of machine learning is similar to that of data mining. Both systems search through data to look for patterns. However, instead of extracting data for human comprehension -- as is the case in data mining applications -- machine learning uses that data to detect patterns in data and adjust program actions accordingly. Example: Facebook's News Feed uses machine learning to personalize each member's feed.
Topics to be covered in Workshop (20% Theory & 80% Hands-On Session)
Module 1
- Introduction to Artificial Intelligence
- Applications of AI & Current trends
- Different AI Techniques
- AI Agents
- PEAS Analysis
- Agent Environment Analysis
- Different Types of AI Agents
- Machine Learning
- Introduction and Applications of Machine Learning
- Supervised and Unsupervised Learning
- Classification & Regression Problem
- Clustering, Anomaly Detection
- Getting started with Linear Regression
- Mathematics behind Linear Regression
- Building Linear Model
- Gradient Descent Algorithm
- Error Correction
Module 2
- Getting started with python programming
- Installing Anaconda
- Python variables, lists, tuples and dictionaries
- Control Structure in Python
- Defining Functions in Python
- Using modules and packages
- Numpy for Data computation
- Matlplotlib for Data Visualization
- Pandas for data exploration
- Using scikit-learn
- Creating linear regression models using scikit-learn
Module 3
- Getting Started with Artificial Neural Networks
- Introduction to neurons, weights
- Activation Function
- Input Layers, Hidden Layers and Output Layers
- Single layer perceptron Model
- Multilayer Neural Network
- Back Propagation Algorithm introduction
- Programming Neural Network using Python
- Building Regression models using ANN
- Classification Examples using ANN
Module 4
- K Nearest Neighbor Models
- Using KNN for Data Classification
- Building Models using KNN
- Support Vector Machine – Applications and Mathematics
- Using SVM for classification
- Projects
Projects covered –
- Housing Prices prediction
- Character Recognition Algorithm
- Diabetes Detection Algorithm
- IRIS Clustering
Duration: The duration of this workshop will be two consecutive days, with eight hour session each day in a total of sixteen hours properly divided into theory and hands on sessions.
Certification Policy:
- Certificate of Participant for all the workshop participants.
- At the end of this workshop, a small competition will be organized among the participating students and winners will be awarded with a 'Certificate of Excellence'.
- Certificate of Coordination for the coordinators of the campus workshops.
Eligibility: There are no prerequisites. Anyone interested, can join this workshop.
