🔬ML ResearcherAdvanced
ML Researcher Path
Build deep theoretical foundations for machine learning research. Master the mathematics, methodology, and skills needed for publishing papers and advancing the field. Designed for those pursuing research roles or PhDs.
20 weeks
9 milestones
0 items
Skills You Will Gain
Deep Learning TheoryResearch MethodologyPaper Reading & WritingExperiment DesignMathematical FoundationsNovel Algorithm DevelopmentAcademic Communication
Prerequisites
- →Strong mathematics background (calculus, linear algebra, probability)
- →Programming proficiency in Python
- →Basic machine learning knowledge
- →Experience reading academic papers
Learning Milestones
1
Mathematical Foundations for ML
Master the mathematics underlying modern machine learning.
~30h•0 items
Learning Objectives
- ✓Deep dive into linear algebra (eigendecomposition, SVD, matrix calculus)
- ✓Master probability theory and statistical inference
- ✓Understand optimization theory (convex, non-convex, gradient methods)
- ✓Learn information theory basics (entropy, KL divergence, mutual information)
- ✓Study measure theory foundations for ML
- ✓Apply mathematical concepts to ML problems
Content coming soon
2
Statistical Learning Theory
Understand the theoretical foundations of learning algorithms.
~25h•0 items
Learning Objectives
- ✓Master PAC learning framework
- ✓Understand VC dimension and Rademacher complexity
- ✓Learn generalization bounds and their implications
- ✓Study bias-variance tradeoff rigorously
- ✓Understand regularization from theoretical perspective
- ✓Analyze sample complexity of learning algorithms
Content coming soon
3
Deep Learning Theory
Explore theoretical aspects of deep neural networks.
~25h•0 items
Learning Objectives
- ✓Understand neural network expressivity and approximation
- ✓Study optimization landscape of deep networks
- ✓Learn implicit regularization in deep learning
- ✓Analyze lottery ticket hypothesis and pruning
- ✓Study neural tangent kernels (NTK)
- ✓Understand double descent and modern generalization puzzles
Content coming soon
4
Paper Reading & Critical Analysis
Develop skills to efficiently read and critically analyze ML papers.
~20h•0 items
Learning Objectives
- ✓Master efficient paper reading strategies
- ✓Identify key contributions and limitations
- ✓Understand experimental methodology standards
- ✓Critically evaluate claims and evidence
- ✓Connect papers to broader research landscape
- ✓Build and maintain a research reading habit
Content coming soon
5
Transformer Theory & Analysis
Deep dive into transformer architecture from a research perspective.
~20h•0 items
Learning Objectives
- ✓Analyze attention mechanisms mathematically
- ✓Understand in-context learning phenomena
- ✓Study emergent abilities in large models
- ✓Explore mechanistic interpretability
- ✓Analyze scaling laws and their predictions
- ✓Study efficient transformer variants
Content coming soon
6
Research Methodology
Learn systematic approaches to conducting ML research.
~20h•0 items
Learning Objectives
- ✓Design rigorous experiments with proper baselines
- ✓Implement reproducible research practices
- ✓Use statistical tests for ML experiments
- ✓Handle ablation studies systematically
- ✓Document and version research code
- ✓Navigate research ethics and responsible AI
Content coming soon
7
Paper Reproduction & Benchmarking
Build skills by reproducing important papers and running benchmarks.
~25h•0 items
Learning Objectives
- ✓Select appropriate papers to reproduce
- ✓Handle missing implementation details
- ✓Debug discrepancies in reproduction
- ✓Run fair benchmark comparisons
- ✓Document reproduction findings
- ✓Contribute to reproducibility efforts
Content coming soon
8
Academic Writing & Communication
Master scientific writing and presentation skills.
~18h•0 items
Learning Objectives
- ✓Structure ML papers effectively
- ✓Write clear abstracts and introductions
- ✓Create effective figures and visualizations
- ✓Handle peer review process
- ✓Present research at conferences
- ✓Write rebuttals and respond to reviews
Content coming soon
9
Advanced Topics: Safety & Alignment
Explore cutting-edge research in AI safety and alignment.
~20h•0 items
Learning Objectives
- ✓Understand AI alignment problem formulation
- ✓Study RLHF and its limitations
- ✓Explore Constitutional AI and alternatives
- ✓Analyze interpretability research
- ✓Study robustness and adversarial examples
- ✓Understand AI governance considerations
Content coming soon
Content Summary
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