Course Syllabus
Machine Learning |
| Semester & Location: |
Spring 2024 - DIS Stockholm |
| Type & Credits: |
Core Course - 3 credits |
| Study Tours: |
Copenhagen, London |
| Major Disciplines: |
Computer science, Mathematics, Data Science |
| Prerequisite(s): |
One year of computer science, a course in algorithms and data structures, one course in linear algebra at university level. A course in statistics is recommended. Knowledge of at least one programming language (e.g. Python/Javascript/Java/C++/Matlab). Note: We will be using Python in this course. If you have little or no experience coding in Python, you should either follow a Python tutorial before the course starts, or prepare to invest some hours getting up to speed with the language once we start. |
| Faculty Members: |
Niklas Fürderer (current students please use the Canvas Inbox) |
| Academic Support: | |
| Program Contact: |
Natalia Landázuri, Ph.D. |
| Time & Place: |
Thursdays 14:50-17:45. Room: D409 |
Course Description
Spotify, Klarna, Zettle, King, and Mojang are just a few of many highly successful tech startups from Stockholm. In many of these companies, machine learning plays a central role: Spotify recommending the next song to play, Klarna automating the payment flows on all the world’s web stores, Zettle detecting anomalies in payment transactions, and King having AI bots for testing and adjusting the game levels in the Candy Crush games. Another example of machine learning is ChatGPT, which is currently transforming our society.
Machine learning utilizes data to develop mathematical models capable of solving problems we have not been able to solve before. This course offers a hands-on approach to the theory and practice of machine learning, with real-world applications. It focuses on understanding various types of data, how to preprocess that data to make it useful for machine learning, approaches on how to solve different problems using machine learning, and how to improve a machine learning system.
The course is taught in 9 sessions, 2 x 80-min each. In addition, the you will go on two half-day field studies, one three-day study tour and a week-long study tour.
- An end-to-end project
- Classification
- Training models, Support Vector Machines
- Decision Trees, Ensemble learning and random forests
- Dimensionality Reduction, Unsupervised learning techniques
- Introduction to deep learning
- Ethics in machine learning
- Presentation of project proposal
- Presentation of final project
Learning Objectives
Knowledge/concepts that will be covered, discussed and acquired during the course:
- Fundamental concepts of machine learning, including supervised and unsupervised learning, overfitting, underfitting, bias, and variance
- Various evaluation metrics used in machine learning, such as accuracy, precision, recall, and F1 score
- Different types of machine learning algorithms, such as decision trees, random forests, support vector machines, and neural networks - and what algorithm to choose to a given problem
- Methods for optimizing machine learning models, such as regularization, cross-validation, and hyperparameter tuning
- The ethics of machine learning and its impact on society
Skills that will be acquired during the course:
- Implementing machine learning algorithms using popular libraries such as scikit-learn and Keras
- Preprocessing and cleaning datasets for machine learning tasks
- Training, evaluating, and fine-tuning machine learning models
- Apply appropriate evaluation metrics to assess the performance of machine learning models
- Interpreting and visualizing machine learning results
Faculty
Niklas Fürderer
After his double master's in Data Science, Niklas worked in different healthcare startups in Stockholm and contributed within different data and machine learning projects. He's currently working as a Senior Data Scientist @Crowd Collective where he helps customers and clients either getting more data driven or reaching their next phase in a specific implementation. Niklas' passion lies in new technologies, entrepreneurship as well as everything data related.
Readings
Additional sources include scientific papers, blog posts and videos available online.
Field studies
Possible visits can include:
- Visit to universities and research institutes to learn about the latest research and development in the field of Machine Learning
- Visit to companies leading the development and implementation of Machine Learning.
Approach to Teaching
The course is designed around the principle of constructive alignment. The major components in the course—the exams and the final project —implement this principle by stating clear goals and activities for every session and in the context of the course as a whole.
You are expected to engage actively in classroom discussions, presentations, exercises, and group work. In addition, you will participate in local field studies and extended course-integrated study tours in Sweden and the United Kingdom. These visits give you the opportunity to learn first-hand from leaders in the field of machine learning, to speak with professionals about their cutting-edge work, and to better understand specific approaches to research, development, implementation and utilization of machine learning.
Leading up to each session, you are given a "preparation goal" and a suggested list of materials you can use to reach it. Sessions start with a short lecture (around 1 hour) that introduces the topic of the day. You work through a set of technical exercises. This gives you a clear outcome goal for each session: "show up prepared and complete the exercises". It gives you incentive to be prepared and focus on the work.
Core Course Week and Study Tours
Core course week and study tours are integral parts of the core course. The classroom is “on the road” and theory presented in the classroom is applied in the field. You will travel with classmates and DIS faculty/staff on two study tours: a short study tour during the core course week and a long study tour to relevant European destinations. You are expected to
- participate in all activities
- engage in discussions, ask questions, and contribute to achieving the learning objectives
- be respectful to the destination/location, the speakers, DIS staff, and fellow classmates
- represent self, home university and DIS in a positive light
While on a program study tour, DIS will provide hostel/hotel accommodation, transportation to/from the destination(s), approx. 2 meals per day and entrances, guides, and visits relevant to your area of study or the destination. You will receive a more detailed itinerary prior to departure.
Travel policies: You are required to travel with your group to the destination. If you have to deviate from the group travel plans, you need approval from the program director and the study tours office.
Expectations of the Students
You are expected to reach the preparation goal leading up to each session. If you have little or no experience coding in Python, you should either follow a Python tutorial before the course starts, or prepare to invest some hours getting up to speed with the language once we start. You should have a working laptop computer. It is advised that each machine has a least 4 GB of RAM and a reasonable processor (if it’s bought after 2014 you should be fine). The Unix operating system is preferred (OSX and Linux), but not a necessity.
Evaluation and Grading
When assigning the final grades, your efforts will weigh as follows:
- Active participation: 20% (includes participation in class, exercises and project, as well as behavior beneficial to the learning of your peers)
- Exams: 40% (two exams, each counting for 20%)
- Final project: 40% (10 % project proposal, 30 % project report and presentation)
Active participation: It is mandatory to actively participate and engage in all scheduled sessions and activities in the course. You are expected to actively participate, engage in the topic and with the teacher, the guest lecturer, the hosts of the study visit etc. You are expected to show a behaviour which is inducing to your peers' learning. In group exercises, you are expected to actively participate, engage, help your peers in their learning and contribute equally to the solution. Inappropriate, irrespectful and/or unprofessional behaviour (e.g., sleeping during presentations, being rude towards our hosts during field studies) results in a score of 0 for participation for the entire semester.
Exams: You are required to complete at least two online exams (40% of the final grade, 20% each), which will cover the topics covered in class and exercises up until then. This is subject to change and we might adapt or modify the online exams depending on our progress during the semester.
Final project: From the beginning of the course, you are aware that an important outcome of the course is a project that could add value to your professional portfolio. To accomplish the project, you will work in teams. Each team selects a topic of their interest, and investigate it using data that needs to scraped or downloaded from the Internet. Student teams submit the project in two parts. First, each team must prepare a project proposal which demonstrates that they have a sound plan for their project and have clear hypotheses related to expected outcomes. The proposal presentation/video is a fun exercise that serves as a platform for sharing ideas between groups. We will view them all in class. In addition, it helps you start with a comprehensive idea of an outcome in mind. Second, once the project is finalized, student teams are required to communicate the results in the popular format of a blog post and a presentation of the final solution.
During the project, you are allowed to consult freely with any of the other students and the instructor. Contributions from other students, however, must be acknowledged with citations in your final report, as required by academic standards. Contributions to your presentations must similarly be acknowledged. Needless to say, the right to consult does not include the right to copy — programs, papers, and presentations must be your own original work.
You are expected to respect all submission deadlines. If an submission is turned in after the due date, the grade of the assignment will either be reduced for each day the submission is late.
Academic Regulations
Please make sure to read the Academic Regulations on the DIS website. There you will find regulations on:
DIS - Study Abroad in Scandinavia - www.DISabroad.org
Course Summary:
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