Introduction & Regression I in Pattern Recognition Lecture 1

What is Machine Learning primarily concerned with?

In the context of Machine Learning, what is the input to the learning process?

Which of the following is NOT one of the main areas of Artificial Intelligence mentioned in the text?

What is the fundamental difference between traditional learning and Machine Learning?

What is the primary goal of Machine Learning?

Which of the following is NOT a cognitive function associated with the human mind, according to the text?

What is the focus of Machine Learning process?

Which type of Machine Learning involves tasks driven by regression or classification?

In a Regression problem, what does 'y' typically represent?

What type of Machine Learning is data-driven and involves clustering?

What influences the prediction of stock prices in Regression?

Which factor is not typically considered for predicting house prices in a Machine Learning model?

Machine learning is part of Artificial Intelligence, which enables machines to mimic cognitive functions like learning, problem-solving, and decision-making.
The machine learning process involves Data, Techniques, and Decisions, with applications in Retail, Face/Speech Recognition, Language Translation, Self-driving Cars, and more.

Good quality data is essential for machine learning, which involves datasets and techniques to make decisions.

Supervised Learning: Task-driven, involves regression and classification, with a "right answer" for each example in the data.
Unsupervised Learning: Data-driven, involves clustering, retrieval, and dimensionality reduction.
Reinforcement Learning: Involves learning by interacting with the environment.

Regression involves predicting a continuous value from features, such as predicting salary or stock price.
Examples of regression problems include:
- Predicting salary based on degree, quality of project, etc.
- Predicting stock price based on recent history and news events.

Artificial Intelligence is the science that allows machines to mimic cognitive functions like learning, problem-solving, and decision-making.
AI involves reasoning, problem-solving, knowledge representation, planning, learning, natural language processing, perception, and motion & manipulation.