Dirbtinis intelektas ir mašininis mokymasis (IT102)

Program code:
Teaching language:
Anglų kalba
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Course goals

The goal of this course is to provide you with the necessary technical expertise to critically reflect on the problems that AI systems are meant to be solving, to identify the strengths and weaknesses of existing AI systems, and to build impactful AI systems yourself. Specifically, the course covers three broad topics: (1) Linear and shallow models to develop enough philosophy and critical thinking about practical problem formulation skills, complex modeling objectives and computational frameworks; (2) Expanding these to deep and non-linear models such as gradient boosted trees, convolutional neural networks, and transformers; (3) Evaluating the applicability and ethics of these models on a range of applied problems. All three topics will be heavily relying on writing code, as well as critically reviewing various literature sources from industry and academia.

The course heavily focuses on using Python and Github. Foundational knowledge in statistics, mathematical analysis, and Python programming and Version Control is assumed.

Course results

  • Using linear models develop intuition on generative thinking and Direct Acyclic Graphs (DAGs) for problem formulation and its implication on model construction and objective metrics. Develop critical thinking towards formulating problems from unstructured domain knowledge. Develop critical thinking towards scientific literature.
  • Write clear, reproducible, and well-documented code in Python and the associated machine learning packages, such as, jax, pytorch, botorch, weights and biases.
  • Learn how to systematically and reproducibly produce AI modeling results, collaborate on AI model building in teams, and communicate AI systems capabilities and limitations.
  • Develop intuition on theory and gain practical experience with deep and non-linear models. Revisit classical statistical assumptions about bias-variance tradeoff.
  • Develop intuition on theory, and gain practical experience with gradient boosted trees, and their utility as a general baseline.
  • Develop intuition on theory, and gain practical experience with transfer learning using deep neural networks architectures such as convolutional neural networks, transformers, and their generalizations.
  • Develop intuition on theory, and gain practical experience with Causal Diagrams and explore-exploit problems.
  • Develop intuition on theory, and gain practical experience with ethics concerning AI systems and their implications on society.