Course waiver info for Stanford courses

The C.S. PhD program has a course waiver process, and given that so many of the students come from similar institutions, there should really be a pre-approved list of courses to transfer over for every area, like there is for "Software Systems". But no, I had to fill out the course waiver form in its entirety, and didn't want to risk annoying the approver by leaving fields blank because I assumed they were familiar with the course. But the least I can do is dump the info here for the two courses I did apply for a waiver for (CS 173 and CS 161, using 6.047 and 6.046 from MIT respectively)

General Info

Stanford course to be waived

: CS 173

Advisor name

: Gill Bejerano

Non-Stanford Course Info

University Name

: Massachusetts Institute of Technology

Instructor

: Manolis Kellis

Year

: 2012

Course website

: http://stellar.mit.edu/S/course/6/fa12/6.047/index.html

Link to course assignments

: http://stellar.mit.edu/S/course/6/fa12/6.047/materials.html

Description:

Covers the algorithmic and machine learning foundations of computational biology, combining theory with practice. Principles of algorithm design, influential problems and techniques, and analysis of large-scale biological datasets. Topics include (a) genomes: sequence analysis, gene finding, RNA folding, genome alignment and assembly, database search; (b) networks: gene expression analysis, regulatory motifs, biological network analysis; (c) evolution: comparative genomics, phylogenetics, genome duplication, genome rearrangements, evolutionary theory. These are coupled with fundamental algorithmic techniques including: dynamic programming, hashing, Gibbs sampling, expectation maximization, hidden Markov models, stochastic context-free grammars, graph clustering, dimensionality reduction, Bayesian networks.

Syllabus:

Dynamic Programming, Global and local alignment
Database search, Rapid string matching, BLAST, BLOSUM
Multiple, Progressive, Phylogenetic, Whole-genome alignment
Whole-Genome Comparative genomics: Evolutionary signatures, Duplication
Genome Assembly: Consensus-alignment-overlap, Graph-based assembly
Hidden Markov Models Part 1: Evaluation / Parsing, Viterbi/Forward algorithm
Hidden Markov Models Part 2: PosteriorDecoding / Learning Baum Welch
Structural RNAs: Fold prediction, genome-wide annotation
Transcript structure analysis, Differential Expression, Significance Testing
Small and Large regulatory RNAs: lincRNA, miRNA, piRNA
Expression analysis: Clustering and Classification, K-means, Naïve-Bayes
Clustering: affinity propagation; Classification: Random Forests
Epigenomics: ChIP-Seq, Burrows-Wheeler alignment, Chromatin States
Regulatory Motifs: Discovery, Representation, PBMs, Gibbs Sampling, EM
Expression deconvolution functional data of mixed samples
Network Inference: Physical and functional networks, information integration
The ENCODE Project: Systematic experimentation and integrative genomics
Dimentionality reduction, PCA, Self-Organizing Maps, SVMs
Disease association mapping, GWAS, organismal phenotypes
Quantitative trait mapping, eQTLs, molecular trait variation
Linkage Disequilibrium, Haplotype phasing, variant imputat
Molecular Evolution, Tree Building, Phylogenetic inference
Gene/species trees, reconciliation, recombination graphs
Mutation rate estimation, coalescent, models of evolution
Missing Heritability, Complex Traits, Interpret GWAS, Rank-based enrichment
Recent human evolution: Human history, human selection
Personal Genomics, Disease Epigenomics: Systems approaches to disease
Three-dimentional chromatin interactions: 3C, 5C, HiC, ChIA-Pet
Pharmacogenomics: Network-based systems biology of drug response
Synthetic Biology: Reading and writing genomes and cellular circuits

+ Final project

Textbook list:

No textbook; Kellis was formulating extensive class notes with the help of the students.

Stanford Course Info

Description:

Introduction to computational biology through an informatic exploration of the human genome. Topics include: genome sequencing; functional landscape of the human genome (genes, gene regulation, repeats, RNA genes, epigenetics); genome evolution (comparative genomics, ultraconservation, co-option). Additional topics may include population genetics, personalized genomics, and ancient DNA. Course includes primers on molecular biology, the UCSC Genome Browser, and text processing languages. Guest lectures on current genomic research topics.

Syllabus:

Introductory biology
Protein Coding Genes
UCSC Genome Browser Tutorial
Introduction to Text Processing Tutorial
Non-protein Coding Genes
Transcriptional Activation I
Transcriptional Regulation II
Transcriptional Regulation III
Genome Evolution I: Repeats
Genome Evolution II
Chains & Nets, Conservation & Function
Sequencing, Human Variation, and Disease
Personal Genomics, GSEA/GREAT
Transcription factor binding sites - Functions and Complexes
Population Genetics & Evo-Devo
Ancestral genome-phenotype mapping

Textbook list:

No specific textbook

General Info

Stanford course to be waived

: CS 161

Advisor name

: Serge Plotkin

Non-Stanford Course Info

University Name

: Massachusetts Institute of Technology

Instructor

: Bruce Tidor, Dana Moshkovitz

Year

: 2012

Course website

: http://stellar.mit.edu/S/course/6/sp12/6.046/index.html

Link to course assignments

: http://stellar.mit.edu/S/course/6/sp12/6.046/materials.html (assignments not public)

Description:

Techniques for the design and analysis of efficient algorithms, emphasizing methods useful in practice. Topics include sorting; search trees, heaps, and hashing; divide-and-conquer; dynamic programming; greedy algorithms; amortized analysis; graph algorithms; and shortest paths. Advanced topics may include network flow; computational geometry; number-theoretic algorithms; polynomial and matrix calculations; caching; and parallel computing.

(This is the second algorithms class in the standard sequence)

Syllabus:

Median Finding
Scheduling
Minimum Spanning Trees
Fast Fourier Transform
All-Pairs Shortest Paths
Randomized algorithms, high probability bounds
Hashing
Amortized Analysis
Competitive Analysis
Network Flow
van Emde Boas Data Structure
Disjoint Sets Data Structures
P vs. NP
Approximation Algorithms
Compression
Sub-linear time algorithms
Clustering
Derandomization
Computational Geometry

Textbook list:

Introduction to Algorithms (CLRS)

Stanford Course Info

Description:

Worst and average case analysis. Recurrences and asymptotics. Efficient algorithms for sorting, searching, and selection. Data structures: binary search trees, heaps, hash tables. Algorithm design techniques: divide-and-conquer, dynamic programming, greedy algorithms, amortized analysis, randomization. Algorithms for fundamental graph problems: minimum-cost spanning tree, connected components, topological sort, and shortest paths.

Syllabus:

Algorithmic complexity and analysis (4 lectures)
Randomization, divide and conquer (2 lectures)
Heaps and counting sort (1 lecture)
Hashing (2 lectures)
Tree and graph definitions and properties (1 lecture)
Binary Search Trees (1 lecture)
Greedy Algorithms (2 lectures)
Dynamic programming (3 lectures)
Graph algorithms (4 lectures)

Textbook list:

CLRS