Fundamentals of Artificial Intelligence (5 op)
Toteutuksen tunnus: TT00EM55-3003
Toteutuksen perustiedot
Ajoitus
15.03.2021 - 31.12.2023
Opintopistemäärä
5 op
Virtuaaliosuus
5 op
Toteutustapa
Etäopetus
Yksikkö
ICT ja tuotantotalous
Toimipiste
Karaportti 2
Opetuskielet
- Englanti
Paikat
0 - 500
Koulutus
- Tieto- ja viestintätekniikan tutkinto-ohjelma
Opettaja
- Virve Prami
Vastuuopettaja
Janne Salonen
Ryhmät
-
CareerBoost_TXK_21Career Boost 21 (Tivi)
-
CareerBoost_TXK_22Career Boost 22 (TiVi)
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%