Fundamentals of Artificial Intelligence (5 op)

Aika ja paikka

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

Oppimateriaalit

Online.

Harjoittelu- ja työelämäyhteistyö

N/A

Tenttien ajankohdat ja uusintamahdollisuudet

Online.

Kansainvälisyys

N/A

Toteutuksen valinnaiset suoritustavat

N/A

Arviointimenetelmät ja arvioinnin perusteet

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

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

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

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.