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I was wondering if any one had any suggestions for areas to research which tie with Artificial Intelligence. My interest is AI but since I haven't taken a class in AI. I can't do a project in that.

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If the goal is a data-structures research project that still connects to AI, pick a problem where the structure itself is the main contribution and the AI/ML part is just the workload. That keeps scope manageable without an AI course. , focus on benchmarks and clear metrics (query time, build time, memory, and for approximate methods, recall@k). 's comment points toward applied text-work — that can be handled as an indexing/retrieval project rather than a full ML exercise.

Project ideas (concise scope and how to measure):

  • Approximate nearest-neighbor indexing: implement kd-tree and an LSH-based index and compare build/query time, memory, and recall@k on embedding datasets. See K-d tree and Locality-sensitive hashing. Compare to libraries like FAISS.
  • Streaming sketches for feature counting: implement Bloom filter and Count–min sketch, then measure memory vs error rate on streaming text or feature streams. See Bloom filter and Count%E2%80%93min_sketch.
  • Compact graph/sparse formats: implement CSR vs adjacency-list variants and benchmark neighborhood queries and memory on large graphs (social or knowledge-graph workloads). See Sparse matrix.
  • Text indexes for fast triage: build an inverted index or compressed trie with k-gram support to quickly find and cluster short posts (a practical, non-ML route to the moderation idea). See Trie and Inverted index.

Practical workflow and a tiny benchmark skeleton:

  1. pick one structure, implement from scratch, and implement one well-known baseline.
  2. choose datasets (UCI or word embeddings like GloVe).
  3. measure build time, query time, memory, and accuracy (if approximate).
  4. repeat with varying dimensionality/scale and report results.
import time

def benchmark(index, queries):
    t0 = time.time()
    for q in queries:
        index.query(q)
    dt = time.time() - t0
    print("total:", dt, "avg:", dt/len(queries))

Cautions: kd-trees degrade in high dimensions (see the curse of dimensionality). Always compare to optimized libraries (FAISS, Annoy) and document reproducible experiments.

Member Avatar for ithelp

A good artificial intelligence project would be , detect whenever someone posts a "need a project" type of post and redirect him to forum guideline .

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