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Overview

Introduction

This course gives a broad overview of the problems and methods studied in the field of artificial intelligence.

Please respect the code of honour.

Intended learning outcomes

After completing this course the student should be able to

  1. recall and apply basic concepts in artificial intelligence

  2. solve problems from the AI domain with limited resources in the form of time and computations

  3. formulate and address an AI related scientific problem

  4. demonstrate an insight into the risks of AI and its role in society

  5. present work in writing and orally

so that the student can

  • make use of methods from artificial intelligence in the analysis, design and implementation of computer programs in academic as well as industrial applications

  • in an appropriate way present results and solutions.

Crew 

  • Lecturer and course responsible
  • Teaching assistants (TA)
    • Johannes A. Stork
    • Akshaya Thippur
    • Kaiyu Hang

Examination overview

The examination consists of completing quizzes (individual), homework assignments (in paris) and a projects (in groups of four). 

To receive a passing grade on the course a student needs to pass all quizzes, the home work assignment (HW1, HW2, HW3) as well as the requirements for the project. The project has to be completed in groups.

NOTE: All assignments MUST be completed in English 

For all examinations, we use the CSC code of honour.

Assessment tasks

The examination for the course will consist of the following assessment tasks.

Quizzes

We will have quizzes after key lectures to test that the students can apply the basic concepts. For some content in the course this will be the only assessment task and for others it will be a way to stimulate continuous learning and help motivate students to learn a concept before we go deeper in the course, for example, to be prepared for an exercise or assignment. A quiz has to be completed within a certain, short, amount of time and will be automatically corrected. It only require simple calculations and multiple-choice answers.

HW1

Implement a game-playing agent. This requires students to solve a problem involving a search algorithm and design a heuristic function. Students are allowed to work in pairs but need to present the results individually.

HW2

Implement an HMM-based agent for duck hunt. This requires the students to solve a problem involving uncertain information using HMMs. Students are allowed to work in pairs but need to present the results individually.

HW3

Write an essay on ethics connected to the risks of AI and its role in society.

Project

The project must be performed in groups of four students. The students form groups themselves. The students will be given five example tasks with grading criteria specified for each tasks. The tasks will still be open enough for the students to have to formulate their specific problem but it will be clear what is required and how we should assess the students. They have to formulate a scientific problem and implement a prototype. They need to write a report and present the work orally. They have to ask questions after the presentation and give feedback on the presentation and assess it. For higher grades a student also needs to give feedback and assess another group’s report and work.

Course components

The course has two course components

  • INL1 (Quizzes, HW1, HW2, HW3)

  • PRO1 (Project)

They are both reported into Ladok as P/F. Some additional information about the course components is maintained in CSC’s result reporting system rapp (https://rapp.csc.kth.se/rapp/).

Criteria based grading

We make use of a criteria based grading system. You will not collect points as in most other courses. Instead, to reach a certain grade you should show that you have fulfilled the criteria for that grade. Please look at the page "Examination and grading" for more information.

Self study lectures

The student body of the course has drastically different background knowledge. Since the course started we spent time introducing basic concepts in search and probabilistic reasoning. Starting this year, to be able to use the time we have in the classroom to introduce concepts which are new to must rather than only some we will provide some material for self studies. The initial two self study lectures covers two things that all CS students at KTH will already know about and are not directly related to the course material but which you need to know about.

Content Chapters Downloads 
Code of honour (QUIZ) N/A
How to use the code submission system Kattis N/A
Basic search (QUIZ) 3
Basic probabilistic reasoning (QUIZ) 13

Lectures

The table below gives a rough overview of the lectures. The lecture notes will be posted in conjunction with the lecture. The exact content of each lecture is only tentative and may be adapted along the way. The means of examination of the content is listed for the lectures. 

The chapters refers to the course book "Artificial Intelligence: A Modern Approach" (3rd Edition) by Stuart J. Russell and Peter Norvig.

 # Content Chapters Downloads 
L1  Introduction 1-2 
L2  Search (HW1, HW2, QUIZ), Games (HW1, QUIZ) 3,4 
L3  CSP (QUIZ) 3-6
L4  Ethics (HW3)
L5  Guest lecture: Gabriel Skantze
Natural Language Processing (PROJECT)
22-23
L6  Probabilistic reasoning, Bayesian Networks,
Hidden Markov Models (QUIZ, HW2)
13-15 
L7  Hidden Markov Models (QUIZ, HW2) 13-15 
L8  Logic and Representation of Knowledge (QUIZ) 10-11  
L9  Planning (QUIZ) 7-9, 12
L10  Making decisions (MDP + POMDP) (QUIZ) 16-17
L11  Guest lecture: Stefan Carlsson
Deep Learning (QUIZ)
-
L12  TBD
L13  Robotics and computer vision
Summary of the course.
Research at CVAP What we do and how we kick ass!
24-25

Exercises 

Exercises/tutorials are carried out in smaller groups and allow for more interaction between students and teachers. The exercises also help bridge the gap between what is discussed in the lectures and the assignments but adding some more practical advice. 

# / Week Tutorial Title Content Downloads 
1 / w36  Search

Search

2 / w37 Games Games: minimax, alpha-beta, 
heuristics, testing 
3 / w39  HMM1 HMM: core concepts and algorithms: 
forward pass, backward pass, 
baum-welch; solving examples 
4 / w40  HMM2 HMM: Viterbi algorithm, HMM examples, 
Real world HMM problems, 
HMM problem decomposition 
TBD TBD 

Additional material

The material below is not directly related to AI but we believe that the content is something that students of computer science should know about and which will help you in your work.

Content Downloads, Links, etc
Piping (mainly for HW1: Checkers)
Versioning
Coding tips

Help Sessions

We offer help sessions which  usually in room 22:an (304), at Teknikringen 14. The KTH webpage has a map that can show you the way there. On some occasions the help sessions have to be moved to a different room. To know when we change the room, read the information page about help sessions.

Homework

This course gives a broad overview of the field of AI. The homeworks are intended to give the student a chance to work with the material a bit more hands-on.

There will be 2 homework assignments focused on implementation (HW1+HW2) and an essay focused on ethics (HW3). Please pay attention to the dates for handing them in as we will be strict with the deadlines! 

Project

The project offers you a chance to practice team work and work on a slightly larger problems that require you to work together. Being able to work in a group is an instrumental skill in most jobs both in academia and industry. You will be required to write a report, make a presentation and review another group's report and act as opponents at their presentation. 

The project work should be carried out in groups of 4 people. You should form your own groups. People that have not formed groups by the deadline will be assigned groups.

NOTE: You will be given a deadline to form groups. If you want to be in a certain group, make sure to form the group before the deadline. If you are not a member of a team (enough if team members have invited you to be considered to be part of the team) after that deadline you have forfeited your ability to chose group and will be placed in a group and we will assign a group for you and you will have to live with that. 

NOTE: If your group does not consist of four students at the deadline we will assign a fourth member to your group and you need to accept this person into the group. Member that are only unofficially in the group do not count!