Fundamentals of Artificial Intelligence (5 op)

Tavoitteet

This course is a gentle introduction to the concepts and methodologies in Artificial Intelligence from both theoretical and practical perspectives. This includes designing intelligent agents through techniques such as state-space search methods and Machine Learning. At the end of this course, students are expected to be able to develop intelligent solutions for semi real-world problems through appropriate methods that are discussed in the course. Furthermore, they are expected to gain knowledge and experience to analyze inner working of the methods and customize for specific problems. They will also get familiar with three main branches of Artificial Intelligence such as Computer Vision, Natural Language Processing and Speech processing and they will find their interest among them.

This course is 100% virtual thanks to the comprehensive interactive material and content made for this course.

The student will pass this course after submitting required assignments, quiz, and projects.

Sisältö

1. Introduction:
What is AI? - Philosophy and history of AI - AI concepts and Some Practical Applications - Python Overview

2. Problem Space and Search:
Problem Solving Agent - Uninformed Search - Informed Search – Search in Continuous Space – Beyond Classical Search – Constraint Satisfaction Problem

3. Games:
What are Games? - Mini-Max Algorithm - Alpha-Beta Pruning – Some Famous Game Agents – Game Theory

4. Machine Learning:
Introduction to Probability - What is Machine Learning? - Classification and regression – Neural Network -

5. Markov Models:
Markov Decision Process - Hidden Markov Model

6. Reinforcement Learning:
Introduction - Passive Learning vs Active Learning – Exploration and Decision Making

6. Applications:
Natural Language Processing – Speech Processing – Computer Vision

Aika ja paikka

Course is in TechClass environment and it can be done in own pace.

Oppimateriaalit

Online.

Opetusmenetelmät

This course is 100% virtual thanks to the comprehensive interactive material and content made for this course.

Course includes:
- Tutorial Videos
- Assignments
- Quiz
- Projects
- Self-study

Harjoittelu- ja työelämäyhteistyö

N/A

Tenttien ajankohdat ja uusintamahdollisuudet

Online.

Kansainvälisyys

N/A

Toteutuksen valinnaiset suoritustavat

N/A

Opiskelijan ajankäyttö ja kuormitus

Lectures = 40h
Assignments = 25h
Self-study = 40h
Quiz = 5h
Project = 20h
Essay = 5h
Total = 135 hours

Sisällön jaksotus

Lectures = 40h
Assignments = 25h
Self-study = 40h
Quiz = 5h
Project = 20h
Essay = 5h
Total = 135 hours

Arviointiasteikko

0-5

Arviointikriteerit, tyydyttävä (1)

- The student knows the basic definitions of Artificial Intelligence and applications of AI.
- The student knows different types of Artificial Intelligence agents and environments.
- The student knows different type of search.
- The student knows what games are.
- The student knows the what is machine learning and different types of it.
- The student is familiar with Probability and Python.

Arviointikriteerit, hyvä (3)

- The student knows different type of search in detail and when to use them.
- The student knows strategies for winning a game.
- The student knows Linear and Logistic Regression and Naïve Bayes.
- The student knows what a Markov Model is and can model real life problem with it.
- The student knows what Neural Network.
- The student knows what Reinforcement Learning is difference of it with Machine Learning.

Arviointikriteerit, kiitettävä (5)

- The student can implement different type of search such as DFS, BFS, etc.
- The student knows what Convex Optimization is and can optimize a convex function.
- The student knows what Game Theory is and can find Nash Equilibrium of it.
- The student knows how to implement Linear Regression, Logistic Regression and Naïve Bayes.
- The student knows elements of a Hidden Markov Model and can model real life problem with it.
- The student knows difference between Passive Learning and Active Learning.
- The student knows some basic intuition about Computer Vision, NLP and Speech Processing.

Arviointimenetelmät ja arvioinnin perusteet

Exercises 30%
Quizzes 20%
Project 40%
Essay 10%