ACIT4630 Advanced Machine Learning and Deep Learning Course description

The following coursework is compulsory and must be approved before the student can take the exam:

• 4-6 days laboratory course
• 2 obligatory classes (4 hours)
• peer-review assessment oral presentations (ethics assignment) (2-4 hours)
• 2 lab reports in groups of 2-4 students

Kurset belyser empiriske og teoretiske problem i historiske så vel som samtidige sammenhenger mot det skandinaviske språkområdet og de nordiske litterære offentligheter og sub-offentligheter som horisont. Det er orientert mot hvordan humanistiske perspektiver kan løfte fram nye problemstillinger og bidra med andre innsikter til spørsmål om forholdene mellom litteratur, lesing og formidling på den ene siden, og demokrati, deltagelse og ytringskulturer på den andre.

Kurset undersøker litteraturens samfunnstilknytninger, hvilke funksjoner litteraturen har, hvordan den anvendes i ulike sosiale, politiske og kulturelle sammenhenger. Demokrati og medborgerskap, lesing og myndiggjøring, digitalisering og nye medier samt globalisering og materielle føringer er noen av de temaene som blir diskutert. Med øye for hvordan språklige, teknologiske og øvrige sosio-materielle forhold spiller inn i en globalisert virkelighet, er kurset særlig opptatt av hvordan humaniora bidrar.

No formal requirements over and above the admission requirements.

On successful completion of the course, students should have the following learning outcomes defined in terms of knowledge, skills, and general competence.

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Knowledge

The student:

• is knowledgeable about supervised, unsupervised, reinforcement learning
• has a good understanding of the principles of state-of-the-art deep neural networks such as CNN, RNN, Transformer, GAN, RL.
• has a good understanding of both theoretical and practical know-how required to use machine learning and deep learning methods effectively

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Skills

The student:

• can build, train, test, and deploy;machine learning and deep learning models
• is able to analyze machine learning methods in regard to their performance and effectiveness
• is able to use existing deep learning networks, improve and/or customize them to apply to new problems

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General competence

The student:

• has both theoretical and practical understanding of machine learning and deep learning methods
• can discuss relevance, strength, and limitations of machine learning and deep learning in solving real-world problems
• is able to work on relevant research projects

The course consists of lectures,;assignments, group consultations, presentation seminars, and project work. In the seminars, students;will present and also actively participate in other presentations. This will facilitate research-oriented education in the field. Research projects will be aimed at cultivating the students towards good future researchers.

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Practical training

None.

The following required coursework must be approved before the student can take the exam:

• Two oral presentations (one on a given topic, one on the topic of own choice)
• Participate as a prepared opponent/discussant in two;presenations from other students

Exam in two parts:

• A group project: implementation and report (about 7000 words). A group of 2-3 students will be formed during the course.;
• Individual oral exam (about 30 minutes).

Each of them carries;50% weight;in the final grade. The oral examination cannot be appealed.

Both exams must be passed in order to pass the course.

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New/postponed exam

In case of failed exam or legal absence, the student may apply for a new or postponed exam. New or postponed exams are offered within a reasonable time span following the regular exam. The student is responsible for applying for a new/postponed exam within the time limits set by OsloMet. The Regulations for new or postponed examinations are available in Regulations relating to studies and examinations at OsloMet.

All aids are permitted for the project report.

No aids are permitted for the oral exam.

Grade scale A-F.

Two internal examiners. External examiner is used periodically.

Associate Professor Raju Shrestha