The session was planned with an intension of giving deep exposure in to the course also covering prescribed university syllabus.He started at l:30P.M with an introduction of resource person
Mr. R.Ajay Kumar , A Technology expert with strong background in robotics and Artificial intelligence innovation through his work as a trainer of Data Point Technologies, A research and development organization focused on the latest technologies.
The speaker has discussed about the Basics concepts of Machine Learning. The elaborated session has happened with interactive session in between including activities, random questioning.
Topics addressed in Guest Lecture:
- Algorithms — Motivation, Genetic algorithms, an illustrative example, hypothesis space
search, genetic programming, models of evolution and learning, parallelizing genetic algorithms.
- Supervised Learning:
In supervised learning, models are trained using labeled data, where each data point is associated with a target or label. The model learns to map input features to these labels.
- Reinforcement Learning — Introduction, the learning task, /—learning, non-deterministic, rewards and actions, temporal difference learning, generalizing from examples, relationship to dynamic programming
- UnSupervised Learning: Unsupervised learning involves finding patterns or structures in data without labeled targets. Common techniques include clustering (grouping similar data points) and dimensionality reduction (reducing the number of features while preserving important information).Algorithms — Motivation, Genetic algorithms, an illustrative example, hypothesis space search, genetic programming, models of evolution and learning, parallelizing genetic algorithms.
- Supervised Learning:
In supervised learning, models are trained using labeled data, where each data point is associated with a target or label. The model learns to map input features to these labels.
- Training Data: This is the portion of the data used to train a machine learning model. The model learns from this data to make predictions or classifications. It typically consists of a large set of labeled examples.
- UnSupervised Learning: Unsupervised learning involves finding patterns or structures in data without labeled targets. Common techniques include clustering (grouping similar data points) and dimensionality reduction (reducing the number of features while preserving important information).
- Reinforcement Learning: In reinforcement learning, agents learn to make a sequence of decisions in an environment to maximize a reward signal. It is commonly used in applications like game playing and robotics.
- Semi-Supervised Learning:
- This is a hybrid approach that combines elements of both supervised and unsupervised learning. It uses a small amount of labeled data along with a larger amount of unlabeled data to train models.IMPACT ANALYSIS Based on the attainments for the batch 2022-2023 on Machine Learning of the A.Y. 2023-2024 III- II SEM, the course was not mapped with the program outcomes. Also, based on the feedback of the students and some teachers, the gap has been identified. So, to fulfil it, we have organized the Guest Lecture for the A.Y. 2023-2024.The resource person Mr. R. Ajay Kumar.The content organized based on the inputs provided by the students in a sequence manner. The speaker has discussed about the concepts of the development of algorithms and statistical models that enable computer systems to improve their performance on a specific task through learning from data, without being explicitly programmed. After attending the session, the students were gained the knowledge on some key important features. Based on their knowledge and skill, the students were presenting their outcomes as follows
